diff --git a/source/_data/pub.bib b/source/_data/pub.bib index 77263f8..02efeb8 100644 --- a/source/_data/pub.bib +++ b/source/_data/pub.bib @@ -7986,3 +7986,4589 @@ @Article{He_ActaMaterialia_2024_v262_p119416 doi = {10.1016/j.actamat.2023.119416}, } +@Article{Marcolongo_Chemsystemschem_2020_v2, + author = {Aris Marcolongo and Tobias Binninger and Federico Zipoli and Teodoro + Laino}, + title = {{Simulating Diffusion Properties of Solid{-}State Electrolytes via a + Neural Network Potential: Performance and Training Scheme}}, + journal = {Chemsystemschem}, + year = 2020, + volume = 2, + number = 3, + doi = {10.1002/syst.201900031}, + abstract = {AbstractThe + recently published DeePMD model, based on a deep neural network + architecture, brings the hope of solving the time{-}scale issue which + often prevents the application of first principle molecular dynamics + to physical systems. With this contribution we assess the performance + of the DeePMD potential on a real{-}life application and model + diffusion of ions in solid{-}state electrolytes. We consider as test + cases the well known Li10{\ens + uremath{<}}/jats:sub>GeP}}2S12, Li{ + \ensuremath{<}}jats:sub>7}}La3< + /jats:sub>Zr2{\ + ensuremath{<}}/jats:sub>O}}12 and Na{\ensurema + th{<}}jats:sub>3 + }}Zr2Si2{\ensuremat + h{<}}/jats:sub>PO + }}12. We develop and test a + training protocol suitable for the computation of diffusion + coefficients, which is one of the key properties to be optimized for + battery applications, and we find good agreement with previous + computations. Our results show that the DeePMD model may be a + successful component of a framework to identify novel solid{-}state + electrolytes.}, +} + +@Article{Xu_JPhysChemC_2023_v127_p24106, + author = {Longkun Xu and Wei Shao and Haishun Jin and Qiang Wang}, + title = {{Data Efficient and Stability Indicated Sampling for Developing + Reactive Machine Learning Potential to Achieve Ultralong Simulation in + Lithium-Metal Batteries}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + number = 50, + pages = {24106--24117}, + doi = {10.1021/acs.jpcc.3c05522}, +} + +@Article{Wu_JPhysChemLett_2023_v14_p11465, + author = {Yu Wu and Ying Chen and Zhenxing Fang and Yimin Ding and Qiaoqiao Li + and Kui Xue and Hezhu Shao and Hao Zhang and Liujiang Zhou}, + title = {{Ultralow Lattice Thermal Transport and Considerable Wave-like Phonon + Tunneling in Chalcogenide Perovskite BaZrS3}}, + journal = {J. Phys. Chem. Lett.}, + year = 2023, + volume = 14, + number = 50, + pages = {11465--11473}, + doi = {10.1021/acs.jpclett.3c02940}, + abstract = {Chalcogenide perovskites provide a promising avenue for nontoxic, + stable thermoelectric materials. Here, the thermal transport and + thermoelectric properties of BaZrS3 as a typical orthorhombic + perovskite are investigated. An extremely low lattice thermal + conductivity {\ensuremath{\kappa}}L of 1.84 W/mK at 300 K is revealed + for BaZrS3, due to the softening effect of Ba atoms on the lattice and + the strong anharmonicity caused by the twisted structure. We + demonstrate that coherence contributions to {\ensuremath{\kappa}}L, + arising from wave-like phonon tunneling, lead to an 18{\%} thermal + transport contribution at 300 K. The increasing temperature softens + the phonons, thus reducing the group velocity of materials and + increasing the scattering phase space. However, it simultaneously + reduces the anharmonicity, which is dominant in BaZrS3 and ultimately + improves the particle-like thermal transport. In addition, via + replacement of the S atom with Se- and Ti-alloying strategy, the ZT + value of BaZrS3 is significantly increased from 0.58 to 0.91 at 500 K, + making it an important candidate for thermoelectric applications.}, +} + +@Article{Hu_CellRepPhysSci_2023_v4_p101713, + author = {Zheng Hu and Hui Li and Wenbo Zhao and Wei Zhou and Shi Hu}, + title = {{Microstructure determination of PdRu immiscible alloys based on + electron-pair distribution function and local elemental segregation}}, + journal = {Cell Rep. Phys. Sci.}, + year = 2023, + volume = 4, + number = 12, + pages = 101713, + doi = {10.1016/j.xcrp.2023.101713}, +} + +@Article{Loose_JPhysChemB_2023_v127_p10564, + author = {Timothy D. Loose and Patrick G. Sahrmann and Thomas S. Qu and Gregory + A. Voth}, + title = {{Coarse-Graining with Equivariant Neural Networks: A Path Toward + Accurate and Data-Efficient Models}}, + journal = {J. Phys. Chem., B}, + year = 2023, + volume = 127, + number = 49, + pages = {10564--10572}, + doi = {10.1021/acs.jpcb.3c05928}, + abstract = {Machine learning has recently entered into the mainstream of coarse- + grained (CG) molecular modeling and simulation. While a variety of + methods for incorporating deep learning into these models exist, many + of them involve training neural networks to act directly as the CG + force field. This has several benefits of which the most significant + is accuracy. Neural networks can inherently incorporate multibody + effects during the calculation of CG forces, and a well-trained neural + network force field outperforms pairwise basis sets generated from + essentially any methodology. However, this comes at a significant + cost. First, these models are typically slower than pairwise force + fields, even when accounting for specialized hardware, which + accelerates the training and integration of such networks. The second + and the focus of this paper is the need for a considerable amount of + data to train such force fields. It is common to use 10s of + microseconds of molecular dynamics data to train a single CG model, + which approaches the point of eliminating the CG model's usefulness in + the first place. As we investigate in this work, this {''}data- + hunger{''} trap from neural networks for predicting molecular energies + and forces can be remediated in part by incorporating equivariant + convolutional operations. We demonstrate that, for CG water, networks + that incorporate equivariant convolutional operations can produce + functional models using data sets as small as a single frame of + reference data, while networks without these operations cannot.}, +} + +@Article{Bai_NanoLett_2023_v23_p10922, + author = {Dongyu Bai and Yihan Nie and Jing Shang and Junxian Liu and Minghao + Liu and Yang Yang and Haifei Zhan and Liangzhi Kou and Yuantong Gu}, + title = {{Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First- + Principles and Deep Learning Molecular Dynamics Simulations}}, + journal = {Nano Lett.}, + year = 2023, + volume = 23, + number = 23, + pages = {10922--10929}, + doi = {10.1021/acs.nanolett.3c03160}, + abstract = {Despite its prevalence in experiments, the influence of complex strain + on material properties remains understudied due to the lack of + effective simulation methods. Here, the effects of bending, rippling, + and bubbling on the ferroelectric domains are investigated in an + In2Se3 monolayer by density functional theory and deep learning + molecular dynamics simulations. Since the ferroelectric switching + barrier can be increased (decreased) by tensile (compressive) strain, + automatic polarization reversal occurs in {\ensuremath{\alpha}}-In2Se3 + with a strain gradient when it is subjected to bending, rippling, or + bubbling deformations to create localized ferroelectric domains with + varying sizes. The switching dynamics depends on the magnitude of + curvature and temperature, following an Arrhenius-style relationship. + This study not only provides a promising solution for cross-scale + studies using deep learning but also reveals the potential to + manipulate local polarization in ferroelectric materials through + strain engineering.}, +} + +@Article{Shayestehpour_JChemTheoryComput_2023_v19_p8732, + author = {Omid Shayestehpour and Stefan Zahn}, + title = {{Efficient Molecular Dynamics Simulations of Deep Eutectic Solvents + with First-Principles Accuracy Using Machine Learning Interatomic + Potentials}}, + journal = {J. Chem. Theory Comput.}, + year = 2023, + volume = 19, + number = 23, + pages = {8732--8742}, + doi = {10.1021/acs.jctc.3c00944}, + abstract = {In recent years, deep eutectic solvents emerged as highly tunable and + ecofriendly alternatives to common organic solvents and liquid + electrolytes. In the present work, the ability of machine learning + (ML) interatomic potentials for molecular dynamics (MD) simulations of + these liquids is explored, showcasing a trained neural network + potential for a 1:2 ratio mixture of choline chloride and urea + (reline). Using the ML potentials trained on density functional theory + data, MD simulations for large systems of thousands of atoms and + nanosecond-long time scales are feasible at a fraction of the + computational cost of the target first-principles simulations. The + obtained structural and dynamical properties of reline from MD + simulations using our machine learning models are in good agreement + with the first-principles MD simulations and experimental results. + Running a single MD simulation is highlighted as a general shortcoming + of typical first-principles studies if the dynamic properties are + investigated. Furthermore, velocity cross-correlation functions are + employed to study the collective dynamics of the molecular components + in reline.}, +} + +@Article{He_PhysRevB_2023_v108_p224114, + author = {Ri He and Hongyu Wu and Xuejian Qin and Xuejiao Chen and Zhicheng + Zhong}, + title = {{Pressure-induced one-dimensional oxygen ion diffusion channel in lead + apatite}}, + journal = {Phys. Rev. B}, + year = 2023, + volume = 108, + number = 22, + pages = 224114, + doi = {10.1103/PhysRevB.108.224114}, + abstract = {Recently, Lee et al. claimed that the experimental observation of + room-temperature ambient-pressure superconductivity in a Cu-doped + lead-apatite (Pb10-xCux(PO4)6O). The study revealed the Cu doping + induces a chemical pressure, resulting in a structural contraction of + one-dimensional Cu-O-Cu atomic column. This unique structure promotes + a one-dimensional electronic conduction channel along the c-axis + mediated by the O atoms, which may be related to superconductivity. + These O atoms occupy 1/4 of the equivalent positions along the c-axis + and exhibit a low diffusion activation energy of 0.8 eV, indicating + the possibility of diffusion between these equivalent positions. Here, + using machine-learning based deep potential, we predict the pressure- + induced fast diffusion of 1/4-occupied O atoms along the one- + dimensional channel in Pb10(PO4)6O at 500 K, while the frameworks of + Pb triangles and PO4 tetrahedrons remain stable. The calculation + results also indicate Cu doping can provide appropriate effective + chemical pressure, indicating the one-dimensional ion diffusion + channel may appear in Pb9Cu(PO4)6O, even at ambient pressure. Our + finding shows that the one-dimensional ions diffusion channel may be + an important factor to affects the fabrication and electrical + measurement of doped lead-apatite.}, +} + +@Article{Crippa_MachLearnSciTechnol_2023_v4_p45044, + author = {Martina Crippa and Annalisa Cardellini and Matteo Cioni and G{\'a}bor + Cs{\'a}nyi and Giovanni M Pavan}, + title = {{Machine learning of microscopic structure-dynamics relationships in + complex molecular systems}}, + journal = {Mach, Learn.,: Sci, Technol,}, + year = 2023, + volume = 4, + number = 4, + pages = 45044, + doi = {10.1088/2632-2153/ad0fa5}, + abstract = {Abstract + In many complex molecular + systems, the macroscopic ensemble{\textquoteright}s properties are + controlled by microscopic dynamic events (or fluctuations) that are + often difficult to detect via pattern-recognition approaches. + Discovering the relationships between local structural environments + and the dynamical events originating from them would allow unveiling + microscopic-level structure-dynamics relationships fundamental to + understand the macroscopic behavior of complex systems. Here we show + that, by coupling advanced structural (e.g. Smooth Overlap of Atomic + Positions, SOAP) with local dynamical descriptors (e.g. Local + Environment and Neighbor Shuffling, LENS) in a unique dataset, it is + possible to improve both individual SOAP- and LENS-based analyses, + obtaining a more complete characterization of the system under study. + As representative examples, we use various molecular systems with + diverse internal structural dynamics. On the one hand, we demonstrate + how the combination of structural and dynamical descriptors + facilitates decoupling relevant dynamical fluctuations from noise, + overcoming the intrinsic limits of the individual analyses. + Furthermore, machine learning approaches also allow extracting from + such combined structural/dynamical dataset useful microscopic-level + relationships, relating key local dynamical events (e.g. LENS + fluctuations) occurring in the systems to the local structural (SOAP) + environments they originate from. Given its abstract nature, we + believe that such an approach will be useful in revealing hidden + microscopic structure-dynamics relationships fundamental to + rationalize the behavior of a variety of complex systems, not + necessarily limited to the atomistic and molecular + scales.}, +} + +@Article{San_NatCommun_2023_v14_p7858, + author = {Xingyuan San and Junwei Hu and Mingyi Chen and Haiyang Niu and Paul J. + M. Smeets and Christos D. Malliakas and Jie Deng and Kunmo Koo and + Roberto {\{}Dos Reis{\}} and Vinayak P. Dravid and Xiaobing Hu}, + title = {{Unlocking the mysterious polytypic features within vaterite CaCO3}}, + journal = {Nat. Commun.}, + year = 2023, + volume = 14, + number = 1, + pages = 7858, + doi = {10.1038/s41467-023-43625-0}, + abstract = {Calcium carbonate (CaCO3), the most abundant biogenic mineral on + earth, plays a crucial role in various fields such as hydrosphere, + biosphere, and climate regulation. Of the four polymorphs, calcite, + aragonite, vaterite, and amorphous CaCO3, vaterite is the most + enigmatic one due to an ongoing debate regarding its structure that + has persisted for nearly a century. In this work, based on systematic + transmission electron microscopy characterizations, crystallographic + analysis and machine learning aided molecular dynamics simulations + with ab initio accuracy, we reveal that vaterite can be regarded as a + polytypic structure. The basic phase has a monoclinic lattice + possessing pseudohexagonal symmetry. Direct imaging and atomic-scale + simulations provide evidence that a single grain of vaterite can + contain three orientation variants. Additionally, we find that + vaterite undergoes a second-order phase transition with a critical + point of {\textasciitilde}190{\,}K. These atomic scale insights + provide a comprehensive understanding of the structure of vaterite and + offer advanced perspectives on the biomineralization process of + calcium carbonate.}, +} + +@Article{Zeng_NpjComputMater_2023_v9_p213, + author = {Qiyu Zeng and Bo Chen and Shen Zhang and Dongdong Kang and Han Wang + and Xiaoxiang Yu and Jiayu Dai}, + title = {{Full-scale ab initio simulations of laser-driven atomistic dynamics}}, + journal = {Npj Comput. Mater}, + year = 2023, + volume = 9, + number = 1, + pages = 213, + doi = {10.1038/s41524-023-01168-4}, + abstract = {AbstractThe + coupling of excited states and ionic dynamics is the basic and + challenging point for the materials response at extreme conditions. In + the laboratory, the intense laser produces transient nature and + complexity with highly nonequilibrium states, making it extremely + difficult and interesting for both experimental measurements and + theoretical methods. With the inclusion of laser-excited states, we + extend an ab initio method into the direct simulations of whole laser- + driven microscopic dynamics from solid to liquid. We construct the + framework of combining the electron-temperature-dependent deep neural- + network potential energy surface with a hybrid atomistic-continuum + approach, controlling non-adiabatic energy exchange and atomistic + dynamics, which enables consistent interpretation of experimental + data. By large-scale ab initio simulations, we demonstrate that the + nonthermal effects introduced by hot electrons play a dominant role in + modulating the lattice dynamics, thermodynamic pathway, and structural + transformation. We highlight that the present work provides a path to + realistic computational studies of laser-driven processes, thus + bridging the gap between experiments and + simulations.}, +} + +@Article{deVilla_NatCommun_2023_v14_p7580, + author = {Kyla {\{}de Villa{\}} and Felipe Gonz{\'a}lez-Cataldo and Burkhard + Militzer}, + title = {{Double superionicity in icy compounds at planetary interior conditions}}, + journal = {Nat. Commun.}, + year = 2023, + volume = 14, + number = 1, + pages = 7580, + doi = {10.1038/s41467-023-42958-0}, + abstract = {The elements hydrogen, carbon, nitrogen and oxygen are assumed to + comprise the bulk of the interiors of the ice giant planets Uranus, + Neptune, and sub-Neptune exoplanets. The details of their interior + structures have remained largely unknown because it is not understood + how the compounds H2O, NH3 and CH4 behave and react once they have + been accreted and exposed to high pressures and temperatures. Here we + study thirteen H-C-N-O compounds with ab initio computer simulations + and demonstrate that they assume a superionic state at elevated + temperatures, in which the hydrogen ions diffuse through a stable + sublattice that is provided by the larger nuclei. At yet higher + temperatures, four of the thirteen compounds undergo a second + transition to a novel doubly superionic state, in which the smallest + of the heavy nuclei diffuse simultaneously with hydrogen ions through + the remaining sublattice. Since this transition and the melting + transition at yet higher temperatures are both of first order, this + may introduce additional layers in the mantle of ice giant planets and + alter their convective patterns.}, +} + +@Article{Guo_JChemPhys_2023_v159_p204702, + author = {Fangyu Guo and Bo Chen and Qiyu Zeng and Xiaoxiang Yu and Kaiguo Chen + and Dongdong Kang and Yong Du and Jianhua Wu and Jiayu Dai}, + title = {{Microstructure evolution under thermo-mechanical operating of + rocksalt-structure TiN via neural network potential}}, + journal = {J. Chem. Phys.}, + year = 2023, + volume = 159, + number = 20, + pages = 204702, + doi = {10.1063/5.0171528}, + abstract = {In the process of high temperature service, the mechanical properties + of cutting tools decrease sharply due to the peeling of the protective + coating. However, the mechanism of such coating failure remains + obscure due to the complicated interaction between atomic structure, + temperature, and stress. This dynamic evolution nature demands both + large system sizes and accurate description on the atomic scale, + raising challenges for existing atomic scale calculation methods. + Here, we developed a deep neural network (DNN) potential for Ti-N + binary systems based on first-principles study datasets to achieve + quantum-accurate large-scale atomic simulation. Compared with + empirical interatomic potential based on the embedded-atom-method, the + developed DNN-potential can accurately predict lattice constants, + phonon properties, and mechanical properties under various + thermodynamic conditions. Moreover, for the first time, we present the + atomic evolution of the fracture behavior of large-scale rocksalt- + structure (B1) TiN systems coupled with temperature and stress + conditions. Our study validates that interatomic brittle fractures + occur when TiN stretches beyond its tensile yield point. Such + simulation of coating fracture and cutting behavior based on large- + scale atoms can shed new light on understanding the microstructure and + mechanical properties of coating tools under extreme operating + conditions.}, +} + +@Article{Lim_JPhysChemC_2023_v127_p22692, + author = {Jinyoung Lim and Youngseon Shim and Jaehong Park and Hongkee Yoon and + Munbo Shim and Young-Gu Kim and Dae Sin Kim}, + title = {{Molecular Dynamics Study of Silicon Carbide Using an Ab Initio-Based + Neural Network Potential: Effect of Composition and Temperature on + Crystallization Behavior}}, + journal = {J. Phys. Chem. C}, + year = 2023, + volume = 127, + number = 46, + pages = {22692--22703}, + doi = {10.1021/acs.jpcc.3c04224}, +} + +@Article{delaPuente_JAmChemSoc_2023_v145_p25186, + author = {Miguel {\{}de la Puente{\}} and Damien Laage}, + title = {{How the Acidity of Water Droplets and Films Is Controlled by the Air- + Water Interface}}, + journal = {J. Am. Chem. Soc.}, + year = 2023, + volume = 145, + number = 46, + pages = {25186--25194}, + doi = {10.1021/jacs.3c07506}, + abstract = {Acidity is a key determinant of chemical reactivity in atmospheric + aqueous aerosols and water microdroplets used for catalysis. However, + many fundamental questions about these systems have remained elusive, + including how their acidity differs from that of bulk solutions, the + degree of heterogeneity between their core and surface, and how the + acid-base properties are affected by their size. Here, we perform + hybrid density functional theory (DFT)-quality neural network-based + molecular simulations with explicit nuclear quantum effects and + combine them with an analytic model to describe the pH and self-ion + concentrations of droplets and films for sizes ranging from nm to + {\ensuremath{\mu}}m. We determine how the acidity of water droplets + and thin films is controlled by the properties of the air-water + interface and by their surface-to-volume ratio. We show that while the + pH is uniform in each system, hydronium and hydroxide ions exhibit + concentration gradients that span the two outermost molecular layers, + enriching the interface with hydronium cations and depleting it with + hydroxide anions. Acidity depends strongly on the surface-to-volume + ratio for system sizes below a few tens of nanometers, where the core + becomes enriched in hydroxide ions and the pH increases as a result of + hydronium stabilization at the interface. These results obtained for + pure water systems have important implications for our understanding + of chemical reactivity in atmospheric aerosols and for catalysis in + aqueous microdroplets.}, +} + +@Article{Xiao_JApplPhys_2023_v134, + author = {R. L. Xiao and K. L. Liu and Y. Ruan and L. Hu and B. Wei}, + title = {{Tutorial: Deep learning prediction of thermophysical properties for + liquid multicomponent alloys}}, + journal = {J. Appl. Phys.}, + year = 2023, + volume = 134, + number = 19, + doi = {10.1063/5.0173250}, + abstract = {The thermophysical properties of + liquid metals and alloys are crucial to explore the intrinsic + mechanisms of the solidification process, glass formation, and fluid + dynamics. The deep learning approaches have emerged as powerful tools + in numerous scientific fields and exhibit extraordinary accuracy in + the estimation of physical properties and structural characteristics + for various materials. In this Tutorial, focusing on the + thermophysical properties of liquid multicomponent alloys, deep + learning methods, including both supervised learning and active + learning, are introduced. Combined with the verification from + electrostatic and electromagnetic levitation experiments, the + influences of training parameters and methods on the accuracy to + obtain interatomic potential by deep learning are revealed on the + basis of deep neural network algorithm. As a result, this prediction + method of liquid state properties for multicomponent alloys exhibited + the dual advantages of high accuracy derived from density functional + theory and low computational cost associated with empirical + potential.}, +} + +@Article{Gromoff_Nanoscale_2023_v16_p384, + author = {Quentin Gromoff and Patrizio Benzo and Wissam A. Saidi and Christopher + M. Andolina and Marie-Jos{\'e} Casanove and Teresa Hungria and Sophie + Barre and Magali Benoit and Julien Lam}, + title = {{Exploring the formation of gold/silver nanoalloys with gas-phase + synthesis and machine-learning assisted simulations}}, + journal = {Nanoscale}, + year = 2023, + volume = 16, + number = 1, + pages = {384--393}, + doi = {10.1039/d3nr04471h}, + abstract = {While nanoalloys are of paramount scientific and practical interest, + the main processes leading to their formation are still poorly + understood. Key structural features in the alloy systems, including + the crystal phase, chemical ordering, and morphology, are challenging + to control at the nanoscale, making it difficult to extend their use + to industrial applications. In this contribution, we focus on the + gold/silver system that has two of the most prevalent noble metals and + combine experiments with simulations to uncover the formation + mechanisms at the atomic level. Nanoparticles were produced using a + state-of-the-art inert-gas aggregation source and analyzed using + transmission electron microscopy and energy-dispersive X-ray + spectroscopy. Machine-learning-assisted molecular dynamics simulations + were employed to model the crystallization process from liquid + droplets to nanocrystals. Our study finds a preponderance of + nanoparticles with five-fold symmetric morphology, including + icosahedra and decahedra which is consistent with previous results on + mono-metallic nanoparticles. However, we observed that gold atoms, + rather than silver atoms, segregate at the surface of the obtained + nanoparticles for all the considered alloy compositions. These + segregation tendencies are in contrast to previous studies and have + consequences on the crystallization dynamics and the subsequent + crystal ordering. We finally showed that the underpinning of this + surprising segregation dynamics is due to charge transfer and + electrostatic interactions rather than surface energy considerations.}, +} + +@Article{Sun_Macromolecules_2023_v56_p9003, + author = {Yaguang Sun and Kaiwei Wan and Wenhui Shen and Jianxin He and Tong + Zhou and Hui Wang and Hua Yang and Xinghua Shi}, + title = {{Modeling Exchange Reactions in Covalent Adaptable Networks with + Machine Learning Force Fields}}, + journal = {Macromolecules}, + year = 2023, + volume = 56, + number = 21, + pages = {9003--9013}, + doi = {10.1021/acs.macromol.3c01377}, +} + +@Article{Chen_JChemTheoryComput_2023_v19_p7861, + author = {Xiangyu Chen and William Shao and Nam Q. Le and Paulette Clancy}, + title = {{Transferable Force Field for Gallium Nitride Crystal Growth from the + Melt Using On-The-Fly Active Learning}}, + journal = {J. Chem. Theory Comput.}, + year = 2023, + volume = 19, + number = 21, + pages = {7861--7872}, + doi = {10.1021/acs.jctc.3c00587}, + abstract = {Atomic-scale simulations of reactive processes have been stymied by + two factors: the lack of a suitable semiempirical force field on one + hand and the impractically large computational burden of using ab + initio molecular dynamics on the other hand. In this paper, we use an + {''}on-the-fly{''} active learning technique to develop a + nonparameterized force field that, in essence, exhibits the accuracy + of density functional theory and the speed of a classical molecular + dynamics simulation. We developed a force field capable of capturing + the crystallization of gallium nitride (GaN) during a novel additive + manufacturing process featuring the reaction of liquid Ga and gaseous + nitrogen precursors to grow crystalline GaN thin films. We show that + this machine learning model is capable of producing a single force + field that can model solid, liquid, and gas phases involved in the + process. We verified our computational predictions against a range of + experimental measurements relevant to each phase and against ab initio + calculations, showing that this nonparametric force field produces + properties with excellent accuracy as well as exhibits computationally + tractable efficiency. The force field is capable of allowing us to + simulate the solid-liquid coexistence interface and the + crystallization of GaN from the melt. The development of this + transferable force field opens the opportunity to simulate the liquid- + phase epitaxial growth more accurately than before to analyze reaction + and diffusion processes and ultimately to establish a growth model of + the additive manufacturing process to create the gallium nitride thin + films.}, +} + +@Article{QiuQiu_ChinPhysLett_2023_v40_p116301, + author = {Rong Rong {\{}Qiu Qiu {\}} and Qiyu Qi Yu {\{}Zeng Ceng {\}} and Han + Han {\{}Wang Wang {\}} and Dongdong Dong Dong {\{}Kang Kang {\}} and + Xiaoxiang Xiao Xiang {\{}Yu Yu {\}} and Jiayu Jia Yu {\{}Dai Dai + {\}}}, + title = {{Anomalous Thermal Transport across the Superionic Transition in Ice}}, + journal = {Chin. Phys, Lett,}, + year = 2023, + volume = 40, + number = 11, + pages = 116301, + doi = {10.1088/0256-307X/40/11/116301}, + abstract = {Superionic ices with highly + mobile protons within stable oxygen sub-lattices occupy an important + proportion of the phase diagram of ice and widely exist in the + interior of icy giants and throughout the Universe. Understanding the + thermal transport in superionic ice is vital for the thermal evolution + of icy planets. However, it is highly challenging due to the extreme + thermodynamic conditions and dynamical nature of protons, beyond the + capability of the traditional lattice dynamics and empirical potential + molecular dynamics approaches. By utilizing the deep potential + molecular dynamics approach, we investigate the thermal conductivity + of ice-VII and superionic ice-VII{''} along the isobar of {\ensuremath + {<}}jats:italic>P}} = 30 GPa. A non-monotonic trend of thermal conductivity with + elevated temperature is observed. Through heat flux decomposition and + trajectory-based spectra analysis, we show that the thermally + activated proton diffusion in ice-VII and superionic ice-VII{''} + contribute significantly to heat convection, while the broadening in + vibrational energy peaks and significant softening of transverse + acoustic branches lead to a reduction in heat conduction. The + competition between proton diffusion and phonon scattering results in + anomalous thermal transport across the superionic transition in ice. + This work unravels the important role of proton diffusion in the + thermal transport of high-pressure ice. Our approach provides new + insights into modeling the thermal transport and atomistic dynamics in + superionic materials.}, +} + +@Article{Wu_PhysRevB_2023_v108_pL180104, + author = {Jing Wu and Jiyuan Yang and Yuan-Jinsheng Liu and Duo Zhang and Yudi + Yang and Yuzhi Zhang and Linfeng Zhang and Shi Liu}, + title = {{Universal interatomic potential for perovskite oxides}}, + journal = {Phys. Rev. B}, + year = 2023, + volume = 108, + number = 18, + pages = {L180104}, + doi = {10.1103/PhysRevB.108.L180104}, + abstract = {With their celebrated structural and chemical flexibility, perovskite + oxides have served as a highly adaptable material platform for + exploring emergent phenomena arising from the interplay between + different degrees of freedom. Molecular dynamics (MD) simulations + leveraging classical force fields, commonly depicted as parameterized + analytical functions, have made significant contributions in + elucidating the atomistic dynamics and structural properties of + crystalline solids including perovskite oxides. However, the force + fields currently available for solids are rather specific and offer + limited transferability, making it time-consuming to use MD to study + new materials systems since a new force field must be parameterized + and tested first. The lack of a generalized force field applicable to + a broad spectrum of solid materials hinders the facile deployment of + MD in computer-aided materials discovery (CAMD). Here, by utilizing a + deep-neural network with a self-attention scheme, we have developed a + unified force field that enables MD simulations of perovskite oxides + involving 14 metal elements and conceivably their solid solutions with + arbitrary compositions. Notably, isobaric-isothermal ensemble MD + simulations with this model potential accurately predict the + experimental phase transition sequences for several markedly different + ferroelectric oxides, including a 6-element ternary solid solution, Pb + (In{\$}{\_}{\{}1/2{\}}{\$}Nb{\$}{\_}{\{}1/2{\}}{\$})O{\$}{\_}3{\$}-- + Pb(Mg{\$}{\_}{\{}1/3{\}}{\$}Nb{\$}{\_}{\{}2/3{\}}{\$})O{\$}{\_}3{\$}-- + PbTiO{\$}{\_}3{\$}. We believe the universal interatomic potential + along with the training database, proposed regression tests, and the + auto-testing workflow, all released publicly, will pave the way for a + systematic improvement and extension of a unified force field for + solids, potentially heralding a new era in CAMD.}, +} + +@Article{Lei_PhysRevB_2023_v108_p184105, + author = {Zhen-Shuai Lei and Xiao-Wei Sun and Xin-Xuan Wang and Zi-Jiang Liu and + Ting Song and Jun-Hong Tian}, + title = {{Influence of temperature on the process of hydrogen bond + symmetrization in $\varepsilon$- + FeOOH}}, + journal = {Phys. Rev. B}, + year = 2023, + volume = 108, + number = 18, + pages = 184105, + doi = {10.1103/PhysRevB.108.184105}, +} + +@Article{Bhatt_PhysRevMater_2023_v7_p115001, + author = {Niraj Bhatt and Pravin Karna and Sandip Thakur and Ashutosh Giri}, + title = {{Transition from electron-dominated to phonon-driven thermal transport + in tungsten under extreme pressures}}, + journal = {Phys, Rev, Mater.}, + year = 2023, + volume = 7, + number = 11, + pages = 115001, + doi = {10.1103/PhysRevMaterials.7.115001}, +} + +@Article{Gong_PhysRevB_2023_v108_p134112, + author = {Zhanpeng Gong and Jefferson Zhe Liu and Xiangdong Ding and Jun Sun and + Junkai Deng}, + title = {{Strain-aided room-temperature second-order ferroelectric phase + transition in monolayer PbTe: Deep potential molecular dynamics + simulations}}, + journal = {Phys. Rev. B}, + year = 2023, + volume = 108, + number = 13, + pages = 134112, + doi = {10.1103/PhysRevB.108.134112}, +} + +@Article{Zhang_AcsPhysChemAu_2024, + author = {Pengchao Zhang and Muye Feng and Xuefei Xu}, + title = {{Double-Layer Distribution of Hydronium and Hydroxide Ions in the + Air{\textendash}Water Interface}}, + journal = {Acs Phys. Chem Au}, + year = 2024, + doi = {10.1021/acsphyschemau.3c00076}, + abstract = {,}, +} + +@Article{Bonati_ProcNatlAcadSciUSA_2023_v120_pe2313023120, + author = {Luigi Bonati and Daniela Polino and Cristina Pizzolitto and + Pierdomenico Biasi and Rene Eckert and Stephan Reitmeier and Robert + Schl{\"o}gl and Michele Parrinello}, + title = {{The role of dynamics in heterogeneous catalysis: Surface diffusivity + and N2 decomposition on Fe(111)}}, + journal = {Proc. Natl. Acad. Sci. U. S. A.}, + year = 2023, + volume = 120, + number = 50, + pages = {e2313023120}, + doi = {10.1073/pnas.2313023120}, + abstract = {Dynamics has long been recognized to play an important role in + heterogeneous catalytic processes. However, until recently, it has + been impossible to study their dynamical behavior at industry-relevant + temperatures. Using a combination of machine learning potentials and + advanced simulation techniques, we investigate the cleavage of the + N[Formula: see text] triple bond on the Fe(111) surface. We find that + at low temperatures our results agree with the well-established + picture. However, if we increase the temperature to reach operando + conditions, the surface undergoes a global dynamical change and the + step structure of the Fe(111) surface is destabilized. The catalytic + sites, traditionally associated with this surface, appear and + disappear continuously. Our simulations illuminate the danger of + extrapolating low-temperature results to operando conditions and + indicate that the catalytic activity can only be inferred from + calculations that take dynamics fully into account. More than that, + they show that it is the transition to this highly fluctuating + interfacial environment that drives the catalytic process.}, +} + +@Article{Liu_PhysRevE_2023_v108_p55310, + author = {K. L. Liu and R. L. Xiao and Y. Ruan and B. Wei}, + title = {{Active learning prediction and experimental confirmation of atomic + structure and thermophysical properties for liquid + Hf{\_}{\{}76{\}}W{\_}{\{}24{\}} refractory alloy}}, + journal = {Phys. Rev., E}, + year = 2023, + volume = 108, + number = {5-2}, + pages = 55310, + doi = {10.1103/PhysRevE.108.055310}, + abstract = {The determination of liquid atomic structure and thermophysical + properties is essential for investigating the physical characteristics + and phase transitions of refractory alloys. However, due to the + stringent experimental requirements and underdeveloped interatomic + potentials, acquiring such information through experimentation or + simulation remains challenging. Here, an active learning method + incorporating a deep neural network was established to generate the + interatomic potential of the Hf{\_}{\{}76{\}}W{\_}{\{}24{\}} + refractory alloy. Then the achieved potential was applied to + investigate the liquid atomic structure and thermophysical properties + of this alloy over a wide temperature range. The simulation results + revealed the distinctive bonding preferences among atoms, that is, Hf + atoms exhibited a strong tendency for conspecific bonding, while W + atoms preferred to form an interspecific bonding. The analysis of + short-range order (SRO) in the liquid alloy revealed a significant + proportion of icosahedral (ICO) and distorted ICO structures, which + even exceeded 30{\%} in the undercooled state. As temperature + decreased, SRO structures demonstrated an increase in larger + coordination number (CN) clusters and a decrease in smaller CNs. The + alterations of the atomic structure indicated that the liquid alloy + becomes more ordered, densely packed, and energetically favorable with + decreasing temperature, consistent with the obtained fact: Both + density and surface tension increase linearly. The simulated + thermophysical properties were close to experimental values with minor + deviations of 2.8{\%} for density and 3.4{\%} for surface tension. The + consistency of the thermophysical properties further attested to the + accuracy and reliability of active learning simulation.}, +} + +@Article{Zhang_NatCommun_2024_v15_p4223, + author = {Xiao-Wei Zhang and Chong Wang and Xiaoyu Liu and Yueyao Fan and Ting + Cao and Di Xiao}, + title = {{Polarization-driven band topology evolution in twisted MoTe2 and WSe2}}, + journal = {Nat. Commun.}, + year = 2024, + volume = 15, + number = 1, + pages = 4223, + doi = {10.1038/s41467-024-48511-x}, + abstract = {Motivated by recent experimental observations of opposite Chern + numbers in R-type twisted MoTe2 and WSe2 homobilayers, we perform + large-scale density-functional-theory calculations with machine + learning force fields to investigate moir{\'e} band topology across a + range of twist angles in both materials. We find that the Chern + numbers of the moir{\'e} frontier bands change sign as a function of + twist angle, and this change is driven by the competition between + moir{\'e} ferroelectricity and piezoelectricity. Our large-scale + calculations, enabled by machine learning methods, reveal crucial + insights into interactions across different scales in twisted bilayer + systems. The interplay between atomic-level relaxation effects and + moir{\'e}-scale electrostatic potential variation opens new avenues + for the design of intertwined topological and correlated states, + including the possibility of mimicking higher Landau level physics in + the absence of magnetic field.}, +} + +@Article{Hedman_NatCommun_2024_v15_p4076, + author = {Daniel Hedman and Ben McLean and Christophe Bichara and Shigeo + Maruyama and J. Andreas Larsson and Feng Ding}, + title = {{Dynamics of growing carbon nanotube interfaces probed by machine + learning-enabled molecular simulations}}, + journal = {Nat. Commun.}, + year = 2024, + volume = 15, + number = 1, + pages = 4076, + doi = {10.1038/s41467-024-47999-7}, + abstract = {Carbon nanotubes (CNTs), hollow cylinders of carbon, hold great + promise for advanced technologies, provided their structure remains + uniform throughout their length. Their growth takes place at high + temperatures across a tube-catalyst interface. Structural defects + formed during growth alter CNT properties. These defects are believed + to form and heal at the tube-catalyst interface but an understanding + of these mechanisms at the atomic-level is lacking. Here we present + DeepCNT-22, a machine learning force field (MLFF) to drive molecular + dynamics simulations through which we unveil the mechanisms of CNT + formation, from nucleation to growth including defect formation and + healing. We find the tube-catalyst interface to be highly dynamic, + with large fluctuations in the chiral structure of the CNT-edge. This + does not support continuous spiral growth as a general mechanism, + instead, at these growth conditions, the growing tube edge exhibits + significant configurational entropy. We demonstrate that defects form + stochastically at the tube-catalyst interface, but under low growth + rates and high temperatures, these heal before becoming incorporated + in the tube wall, allowing CNTs to grow defect-free to seemingly + unlimited lengths. These insights, not readily available through + experiments, demonstrate the remarkable power of MLFF-driven + simulations and fill long-standing gaps in our understanding of CNT + growth mechanisms.}, +} + +@Article{Owen_NpjComputMater_2024_v10_p92, + author = {Cameron J. Owen and Steven B. Torrisi and Yu Xie and Simon Batzner and + Kyle Bystrom and Jennifer Coulter and Albert Musaelian and Lixin Sun + and Boris Kozinsky}, + title = {{Complexity of many-body interactions in transition metals via machine- + learned force fields from the TM23 data set}}, + journal = {Npj Comput. Mater}, + year = 2024, + volume = 10, + number = 1, + pages = 92, + doi = {10.1038/s41524-024-01264-z}, + abstract = {AbstractThis work + examines challenges associated with the accuracy of machine-learned + force fields (MLFFs) for bulk solid and liquid phases of {\ensuremath{ + <}}jats:italic>d}}-block elements. In exhaustive detail, we contrast the + performance of force, energy, and stress predictions across the + transition metals for two leading MLFF models: a kernel-based atomic + cluster expansion method implemented using sparse Gaussian processes + (FLARE), and an equivariant message-passing neural network (NequIP). + Early transition metals present higher relative errors and are more + difficult to learn relative to late platinum- and coinage-group + elements, and this trend persists across model architectures. Trends + in complexity of interatomic interactions for different metals are + revealed via comparison of the performance of representations with + different many-body order and angular resolution. Using arguments + based on perturbation theory on the occupied and unoccupied {\ensurema + th{<}}jats:italic>d}} states near the Fermi level, we determine that the large, + sharp d density of states both above and below the + Fermi level in early transition metals leads to a more complex, + harder-to-learn potential energy surface for these metals. Increasing + the fictitious electronic temperature (smearing) modifies the angular + sensitivity of forces and makes the early transition metal forces + easier to learn. This work illustrates challenges in capturing + intricate properties of metallic bonding with current leading MLFFs + and provides a reference data set for transition metals, aimed at + benchmarking the accuracy and improving the development of emerging + machine-learned approximations.}, +} + +@Article{Fan_NatCommun_2024_v15_p3251, + author = {Dong Fan and Supriyo Naskar and Guillaume Maurin}, + title = {{Unconventional mechanical and thermal behaviours of MOF CALF-20}}, + journal = {Nat. Commun.}, + year = 2024, + volume = 15, + number = 1, + pages = 3251, + doi = {10.1038/s41467-024-47695-6}, + abstract = {CALF-20 was recently identified as a benchmark sorbent for CO2 capture + at the industrial scale, however comprehensive atomistic insight into + its mechanical/thermal properties under working conditions is still + lacking. In this study, we developed a general-purpose machine-learned + potential (MLP) for the CALF-20 MOF framework that predicts the + thermodynamic and mechanical properties of the structure at finite + temperatures within first-principles accuracy. Interestingly, CALF-20 + was demonstrated to exhibit both negative area compression and + negative thermal expansion. Most strikingly, upon application of the + tensile strain along the [001] direction, CALF-20 was shown to display + a distinct two-step elastic deformation behaviour, unlike typical MOFs + that undergo plastic deformation after elasticity. Furthermore, this + MOF was shown to exhibit a fracture strain of up to 27{\%} along the + [001] direction at room temperature comparable to that of MOF glasses. + These abnormal thermal and mechanical properties make CALF-20 as + attractive material for flexible and stretchable electronics and + sensors.}, +} + +@Article{Li_NatCommun_2024_v15_p2653, + author = {Tao Li and Yongyi Wu and Guoliang Yu and Shengxian Li and Yifeng Ren + and Yadong Liu and Jiarui Liu and Hao Feng and Yu Deng and Mingxing + Chen and Zhenyu Zhang and Tai Min}, + title = {{Realization of sextuple polarization states and interstate switching + in antiferroelectric CuInP2S6}}, + journal = {Nat. Commun.}, + year = 2024, + volume = 15, + number = 1, + pages = 2653, + doi = {10.1038/s41467-024-46891-8}, + abstract = {Realization of higher-order multistates with mutual interstate + switching in ferroelectric materials is a perpetual drive for high- + density storage devices and beyond-Moore technologies. Here we + demonstrate experimentally that antiferroelectric van der Waals + CuInP2S6 films can be controllably stabilized into double, quadruple, + and sextuple polarization states, and a system harboring polarization + order of six is also reversibly tunable into order of four or two. + Furthermore, for a given polarization order, mutual interstate + switching can be achieved via moderate electric field modulation. + First-principles studies of CuInP2S6 multilayers help to reveal that + the double, quadruple, and sextuple states are attributable to the + existence of respective single, double, and triple ferroelectric + domains with antiferroelectric interdomain coupling and Cu ion + migration. These findings offer appealing platforms for developing + multistate ferroelectric devices, while the underlining mechanism is + transformative to other non-volatile material systems.}, +} + +@Article{AbouElKheir_NpjComputMater_2024_v10_p33, + author = {Omar {\{}Abou El Kheir{\}} and Luigi Bonati and Michele Parrinello and + Marco Bernasconi}, + title = {{Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change + compound with a machine-learned interatomic potential}}, + journal = {Npj Comput. Mater}, + year = 2024, + volume = 10, + number = 1, + pages = 33, + doi = {10.1038/s41524-024-01217-6}, + abstract = {AbstractThe phase + change compound Ge2{\ensuremat + h{<}}/jats:sub>Sb + }}2Te5 (GST225) is + exploited in advanced non-volatile electronic memories and in + neuromorphic devices which both rely on a fast and reversible + transition between the crystalline and amorphous phases induced by + Joule heating. The crystallization kinetics of GST225 is a key + functional feature for the operation of these devices. We report here + on the development of a machine-learned interatomic potential for + GST225 that allowed us to perform large scale molecular dynamics + simulations (over 10,000 atoms for over 100{\,}ns) to uncover the + details of the crystallization kinetics in a wide range of + temperatures of interest for the programming of the devices. The + potential is obtained by fitting with a deep neural network (NN) + scheme a large quantum-mechanical database generated within density + functional theory. The availability of a highly efficient and yet + highly accurate NN potential opens the possibility to simulate phase + change materials at the length and time scales of the real + devices.}, +} + +@Article{You_NpjComputMater_2024_v10_p57, + author = {Yiwei You and Dexin Zhang and Fulun Wu and Xinrui Cao and Yang Sun and + Zi-Zhong Zhu and Shunqing Wu}, + title = {{Principal component analysis enables the design of deep learning + potential precisely capturing LLZO phase transitions}}, + journal = {Npj Comput. Mater}, + year = 2024, + volume = 10, + number = 1, + pages = 57, + doi = {10.1038/s41524-024-01240-7}, + abstract = {AbstractThe + development of accurate and efficient interatomic potentials using + machine learning has emerged as an important approach in materials + simulations and discovery. However, the systematic construction of + diverse, converged training sets remains challenging. We develop a + deep learning-based interatomic potential for the Li7La{\en + suremath{<}}jats:sub>3}}Zr2O12{\ens + uremath{<}}/jats:sub> (LLZO) system. Our interatomic + potential is trained using a diverse dataset obtained from databases + and first-principles simulations. We propose using the coverage of the + training and test sets as the convergence criteria for the training + iterations, where the coverage is calculated by principal component + analysis. This results in an accurate LLZO interatomic potential that + can describe the structure and dynamical properties of LLZO systems + meanwhile greatly reducing computational costs compared to density + functional theory calculations. The interatomic potential accurately + describes radial distribution functions and thermal expansion + coefficient consistent with experiments. It also predicts the + tetragonal-to-cubic phase transition behaviors of LLZO systems. Our + work provides an efficient training strategy to develop accurate deep- + learning interatomic potential for complex solid-state electrolyte + materials, providing a promising simulation tool to accelerate solid- + state battery design and + applications.}, +} + +@Article{Qi_NpjComputMater_2024_v10_p43, + author = {Ji Qi and Tsz Wai Ko and Brandon C. Wood and Tuan Anh Pham and Shyue + Ping Ong}, + title = {{Robust training of machine learning interatomic potentials with + dimensionality reduction and stratified sampling}}, + journal = {Npj Comput. Mater}, + year = 2024, + volume = 10, + number = 1, + pages = 43, + doi = {10.1038/s41524-024-01227-4}, + abstract = {AbstractMachine + learning interatomic potentials (MLIPs) enable accurate simulations of + materials at scales beyond that accessible by ab initio methods and + play an increasingly important role in the study and design of + materials. However, MLIPs are only as accurate and robust as the data + on which they are trained. Here, we present DImensionality-Reduced + Encoded Clusters with sTratified (DIRECT) sampling as an approach to + select a robust training set of structures from a large and complex + configuration space. By applying DIRECT sampling on the Materials + Project relaxation trajectories dataset with over one million + structures and 89 elements, we develop an improved materials 3-body + graph network (M3GNet) universal potential that extrapolates more + reliably to unseen structures. We further show that molecular dynamics + (MD) simulations with the M3GNet universal potential can be used + instead of expensive ab initio MD to rapidly create a large + configuration space for target systems. We combined this scheme with + DIRECT sampling to develop a reliable moment tensor potential for + titanium hydrides without the need for iterative augmentation of + training structures. This work paves the way for robust high- + throughput development of MLIPs across any compositional + complexity.}, +} + +@Article{Wieser_NpjComputMater_2024_v10_p18, + author = {Sandro Wieser and Egbert Zojer}, + title = {{Machine learned force-fields for an Ab-initio quality description of + metal-organic frameworks}}, + journal = {Npj Comput. Mater}, + year = 2024, + volume = 10, + number = 1, + pages = 18, + doi = {10.1038/s41524-024-01205-w}, + abstract = {AbstractMetal- + organic frameworks (MOFs) are an incredibly diverse group of highly + porous hybrid materials, which are interesting for a wide range of + possible applications. For a meaningful theoretical description of + many of their properties accurate and computationally highly efficient + methods are in high demand. These would avoid compromises regarding + either the quality of modelling results or the level of complexity of + the calculated properties. With the advent of machine learning + approaches, it is now possible to generate such approaches with + relatively little human effort. Here, we build on existing types of + machine-learned force fields belonging to the moment-tensor and + kernel-based potential families to develop a recipe for their + efficient parametrization. This yields exceptionally accurate and + computationally highly efficient force fields. The parametrization + relies on reference configurations generated during molecular dynamics + based, active learning runs. The performance of the potentials is + benchmarked for a representative selection of commonly studied MOFs + revealing a close to DFT accuracy in predicting forces and structural + parameters for a set of validation structures. The same applies to + elastic constants and phonon band structures. Additionally, for MOF-5 + the thermal conductivity is obtained with full quantitative agreement + to single-crystal experiments. All this is possible while maintaining + a very high degree of computational efficiency. The exceptional + accuracy of the parameterized force field potentials combined with + their computational efficiency has the potential of lifting the + computational modelling of MOFs to the next + level.}, +} + +@Article{DeAngelis_SciRep_2024_v14_p978, + author = {Paolo {\{}De Angelis{\}} and Roberta Cappabianca and Matteo Fasano and + Pietro Asinari and Eliodoro Chiavazzo}, + title = {{Enhancing ReaxFF for molecular dynamics simulations of lithium-ion + batteries: an interactive reparameterization protocol}}, + journal = {Sci. Rep.}, + year = 2024, + volume = 14, + number = 1, + pages = 978, + doi = {10.1038/s41598-023-50978-5}, + abstract = {Lithium-ion batteries (LIBs) have become an essential technology for + the green economy transition, as they are widely used in portable + electronics, electric vehicles, and renewable energy systems. The + solid-electrolyte interphase (SEI) is a key component for the correct + operation, performance, and safety of LIBs. The SEI arises from the + initial thermal metastability of the anode-electrolyte interface, and + the resulting electrolyte reduction products stabilize the interface + by forming an electrochemical buffer window. This article aims to make + a first-but important-step towards enhancing the parametrization of a + widely-used reactive force field (ReaxFF) for accurate molecular + dynamics (MD) simulations of SEI components in LIBs. To this end, we + focus on Lithium Fluoride (LiF), an inorganic salt of great interest + due to its beneficial properties in the passivation layer. The + protocol relies heavily on various Python libraries designed to work + with atomistic simulations allowing robust automation of all the + reparameterization steps. The proposed set of configurations, and the + resulting dataset, allow the new ReaxFF to recover the solid nature of + the inorganic salt and improve the mass transport properties + prediction from MD simulation. The optimized ReaxFF surpasses the + previously available force field by accurately adjusting the + diffusivity of lithium in the solid lattice, resulting in a two-order- + of-magnitude improvement in its prediction at room temperature. + However, our comprehensive investigation of the simulation shows the + strong sensitivity of the ReaxFF to the training set, making its + ability to interpolate the potential energy surface challenging. + Consequently, the current formulation of ReaxFF can be effectively + employed to model specific and well-defined phenomena by utilizing the + proposed interactive reparameterization protocol to construct the + dataset. Overall, this work represents a significant initial step + towards refining ReaxFF for precise reactive MD simulations, shedding + light on the challenges and limitations of ReaxFF force field + parametrization. The demonstrated limitations emphasize the potential + for developing more versatile and advanced force fields to upscale ab + initio simulation through our interactive reparameterization protocol, + enabling more accurate and comprehensive MD simulations in the future.}, +} + +@Article{Zhang_JColloidInterfaceSci_2024_v671_p258, + author = {Meng Zhang and Yang Liu and Yun Duan and Xu Liu and Yan-Qin Wang}, + title = {{Ce-doped copper oxide and copper vanadate Cu3VO4 hybrid for boosting + nitrate electroreduction to ammonia}}, + journal = {J. Colloid Interface Sci.}, + year = 2024, + volume = 671, + pages = {258--269}, + doi = {10.1016/j.jcis.2024.05.189}, + abstract = {The electrocatalytic nitrate reduction to ammonia reaction (ENO3RR) + holds great potential as a cost-effective method for synthesizing + ammonia. This work designed a cerium (Ce) doped Cu2+1O/Cu3VO4 + catalyst. The coupling of vanadium-based oxides with Cu2+1O + effectively adjusts the catalyst's electronic structure, addressing + the inherent issues of limited activity and low conductivity in + typical copper-based oxides; moreover, Ce doping generates oxygen + vacancies (Ov), providing more active sites and thereby enhancing the + ENO3RR performance. The catalyst exhibits superior NH3Faradaic + efficiency (93.7{~}{\%}) with a NH3 yield of 18.905{~}mg{~}h-1 cm-2at + -0.5{~}V vs. RHE under alkaline conditions. This study provides + guidance for the design of highly efficient catalysts for ENO3RR.}, +} + +@Article{Wang_ComputMaterSci_2024_v244_p113154, + author = {Xinwei Wang and Zi-Jiang Liu and Jin-Shan Feng and Meng-Ru Chen and + Liang Li and Xiao-Wei Sun and Fubo Tian}, + title = {{Construction and application of deep learning potential for CaO under + high pressure}}, + journal = {Comput. Mater. Sci.}, + year = 2024, + volume = 244, + pages = 113154, + doi = {10.1016/j.commatsci.2024.113154}, +} + +@Article{Bhatt_IntJHeatMassTransf_2024_v229_p125673, + author = {Niraj Bhatt and Pravin Karna and Sandip Thakur and Ashutosh Giri}, + title = {{Pressure-driven enhancement of phonon contribution to the thermal + conductivity of Iridium}}, + journal = {Int. J. Heat Mass Transf.}, + year = 2024, + volume = 229, + pages = 125673, + doi = {10.1016/j.ijheatmasstransfer.2024.125673}, +} + +@Article{Wang_NanoEnergy_2024_v127_p109762, + author = {Jing Wang and Xinrong Yan and Xin Wang and Mingli Yang and Dingguo Xu}, + title = {{Selective activation of methane on hydroxyapatite surfaces: Insights + from machine learning and density functional theory}}, + journal = {Nano Energy}, + year = 2024, + volume = 127, + pages = 109762, + doi = {10.1016/j.nanoen.2024.109762}, +} + +@Article{Feng_SolEnergyMaterSolCells_2024_v272_p112903, + author = {Taixi Feng and Jia Zhao and Guimin Lu}, + title = {{Machine learning model to efficiently predict the structure and + properties of MgCl2{\textendash}NaCl{\textendash}KCl melts}}, + journal = {Sol. Energy Mater. Sol. Cells}, + year = 2024, + volume = 272, + pages = 112903, + doi = {10.1016/j.solmat.2024.112903}, +} + +@Article{Galvani_JPhysMater_2024_v7_p35003, + author = {Thomas Galvani and Ali K Hamze and Laura Caputo and Onurcan Kaya and + Simon M-M Dubois and Luigi Colombo and Viet-Hung Nguyen and Yongwoo + Shin and Hyeon-Jin Shin and Jean-Christophe Charlier and Stephan Roche}, + title = {{Exploring dielectric properties in atomistic models of amorphous boron + nitride}}, + journal = {J. Phys. Mater.}, + year = 2024, + volume = 7, + number = 3, + pages = 35003, + doi = {10.1088/2515-7639/ad4c06}, + abstract = {Abstract + We report a theoretical study of + dielectric properties of models of amorphous Boron Nitride, using + interatomic potentials generated by machine learning. We first perform + first-principles simulations on small (about 100 atoms in the periodic + cell) sample sizes to explore the emergence of mid-gap states and its + correlation with structural features. Next, by using a simplified + tight-binding electronic model, we analyse the dielectric functions + for complex three dimensional models (containing about 10.000 atoms) + embedding varying concentrations of sp}}1, sp< + jats:sup>2 and + sp3}} bonds between B and N atoms. Within the limits of these + methodologies, the resulting value of the zero-frequency dielectric + constant is shown to be influenced by the population density of such + mid-gap states and their localization characteristics. We observe + nontrivial correlations between the structure-induced electronic + fluctuations and the resulting dielectric constant values. Our + findings are however just a first step in the quest of accessing fully + accurate dielectric properties of as-grown amorphous BN of relevance + for interconnect technologies and + beyond.}, +} + +@Article{Che_CeramInt_2024_v50_p22865, + author = {Junwei Che and Wenjie Huang and Guoliang Ren and Jiajun Linghu and + Xuezhi Wang}, + title = {{Dual-channel phonon transport leads to low thermal conductivity in + pyrochlore La2Hf2O7}}, + journal = {Ceram. Int.}, + year = 2024, + volume = 50, + number = 13, + pages = {22865--22873}, + doi = {10.1016/j.ceramint.2024.04.011}, +} + +@Article{Zhang_JMaterSciTechnol_2024_v185_p23, + author = {Linshuang Zhang and Manyi Yang and Shiwei Zhang and Haiyang Niu}, + title = {{Unveiling the crystallization mechanism of cadmium selenide via + molecular dynamics simulation with machine-learning-based deep + potential}}, + journal = {J. Mater. Sci. Technol.}, + year = 2024, + volume = 185, + pages = {23--31}, + doi = {10.1016/j.jmst.2023.09.059}, +} + +@Article{Wang_ComposBEng_2024_v279_p111452, + author = {Kai Wang and Guoqing Yao and Mengwei Lv and Zumin Wang and Yuan Huang + and Wei Xi}, + title = {{The nucleation and growth mechanism of solid-state amorphization and + diffusion behavior at the W{\textendash}Cu interface}}, + journal = {Compos. B: Eng.}, + year = 2024, + volume = 279, + pages = 111452, + doi = {10.1016/j.compositesb.2024.111452}, +} + +@Article{Hua_EnergyStorageMater_2024_v70_p103470, + author = {Haiming Hua and Fei Wang and Feng Wang and Jiayue Wu and Yaoqi Xu and + Yichao Zhuang and Jing Zeng and Jinbao Zhao}, + title = {{Machine learning molecular dynamics insight into high interface + stability and fast kinetics of low-cost magnesium chloride amine + electrolyte for rechargeable magnesium batteries}}, + journal = {Energy Storage Mater.}, + year = 2024, + volume = 70, + pages = 103470, + doi = {10.1016/j.ensm.2024.103470}, +} + +@Article{Chen_WaterRes_2024_v256_p121580, + author = {Kai Chen and Chuling Guo and Chaoping Wang and Shoushi Zhao and Beiyi + Xiong and Guining Lu and John R. Reinfelder and Zhi Dang}, + title = {{Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid + surface complexation-machine learning model}}, + journal = {Water Res.}, + year = 2024, + volume = 256, + pages = 121580, + doi = {10.1016/j.watres.2024.121580}, + abstract = {This study aimed to develop surface complexation modeling-machine + learning (SCM-ML) hybrid model for chromate and arsenate adsorption on + goethite. The feasibility of two SCM-ML hybrid modeling approaches was + investigated. Firstly, we attempted to utilize ML algorithms and + establish the parameter model, to link factors influencing the + adsorption amount of oxyanions with optimized surface complexation + constants. However, the results revealed the optimized chromate or + arsenate surface complexation constants might fall into local extrema, + making it unable to establish a reasonable mapping relationship + between adsorption conditions and surface complexation constants by ML + algorithms. In contrast, species-informed models were successfully + obtained, by incorporating the surface species information calculated + from the unoptimized SCM with the adsorption condition as input + features. Compared with the optimized SCM, the species-informed model + could make more accurate predictions on pH edges, isotherms, and + kinetic data for various input conditions (for chromate: root mean + square error (RMSE) on test set = 5.90 {\%}; for arsenate: RMSE on + test set = 4.84 {\%}). Furthermore, the utilization of the + interpretable formula based on Local Interpretable Model-Agnostic + Explanations (LIME) enabled the species-informed model to provide + surface species information like SCM. The species-informed SCM-ML + hybrid modeling method proposed in this study has great practicality + and application potential, and is expected to become a new paradigm in + surface adsorption model.}, +} + +@Article{Cao_IntJHeatMassTransf_2024_v224_p125359, + author = {Chenyang Cao and Shuo Cao and YuanXu Zhu and Haikuan Dong and Yanzhou + Wang and Ping Qian}, + title = {{Thermal transports of 2D phosphorous carbides by machine learning + molecular dynamics simulations}}, + journal = {Int. J. Heat Mass Transf.}, + year = 2024, + volume = 224, + pages = 125359, + doi = {10.1016/j.ijheatmasstransfer.2024.125359}, +} + +@Article{Shi_JAmCeramSoc_2024_v107_p3845, + author = {Zuhao Shi and Bin Liu and Yuanzheng Yue and Arramel Arramel and Neng + Li}, + title = {{Unraveling medium{-}range order and melting mechanism of ZIF{-}4 under + high temperature}}, + journal = {J Am Ceram Soc.}, + year = 2024, + volume = 107, + number = 6, + pages = {3845--3856}, + doi = {10.1111/jace.19741}, + abstract = {AbstractGlass + formation in zeolitic imidazolate frameworks (ZIFs) has garnered + significant attention in the field of metal{\textendash}organic + frameworks (MOFs) in recent years. Numerous works have been conducted + to investigate the microscopic mechanisms involved in the + melting{\textendash}quenching process of ZIFs. Understanding the + density variations that occur during the melting process of ZIFs is + crucial for comprehending the origins of glass formation. However, + conducting large{-}scale simulations has been challenging due to + limitations in computational resources. In this work, we used + deep{-}learning methods to accurately construct a potential function + that describes the atomic{-}scale melting behavior of ZIF{-}4. The + results revealed the spatial heterogeneity associated with the + formation of low{-}density phases during the melting process of + ZIF{-}4. This work discusses the advantages and limitations of + applying deep{-}learning simulation methods to complex structures like + ZIFs, providing valuable insights for the development of + machine{-}learning approaches in designing MOF + glasses.}, +} + +@Article{Zhang_JEurCeramSoc_2024_v44_p4243, + author = {Jin-Yu Zhang and Ga{\"e}l Huynh and Fu-Zhi Dai and Tristan Albaret and + Shi-Hao Zhang and Shigenobu Ogata and David Rodney}, + title = {{A deep-neural network potential to study transformation-induced + plasticity in zirconia}}, + journal = {J. Eur. Ceram. Soc.}, + year = 2024, + volume = 44, + number = 6, + pages = {4243--4254}, + doi = {10.1016/j.jeurceramsoc.2024.01.007}, +} + +@Article{Sowa_JPhysChemC_2024_v128_p8724, + author = {Jakub K. Sowa and Danielle M. Cadena and Arshad Mehmood and Benjamin + G. Levine and Sean T. Roberts and Peter J. Rossky}, + title = {{IR Spectroscopy of Carboxylate-Passivated Semiconducting Nanocrystals: + Simulation and Experiment}}, + journal = {J. Phys. Chem. C}, + year = 2024, + volume = 128, + number = 21, + pages = {8724--8731}, + doi = {10.1021/acs.jpcc.4c01988}, + abstract = {Surfaces of colloidal nanocrystals are frequently passivated with + carboxylate ligands which exert significant effects on their + optoelectronic properties and chemical stability. Experimentally, + binding geometries of such ligands are typically investigated using + vibrational spectroscopy, but the interpretation of the IR signal is + usually not trivial. Here, using machine-learning (ML) algorithms + trained on DFT data, we simulate an IR spectrum of a lead-rich PbS + nanocrystal passivated with butyrate ligands. We obtain a good + agreement with the experimental signal and demonstrate that the + observed line shape stems from a very wide range of `tilted- + bridge'-type geometries and does not indicate a coexistence of + `bridging' and `chelating' binding modes as has been previously + assumed. This work illustrates limitations of empirical spectrum + assignment and demonstrates the effectiveness of ML-driven molecular + dynamics simulations in reproducing IR spectra of nanoscopic systems.}, +} + +@Article{Wang_JAmChemSoc_2024_v146_p14566, + author = {Feng Wang and Zebing Ma and Jun Cheng}, + title = {{Accelerating Computation of Acidity Constants and Redox Potentials for + Aqueous Organic Redox Flow Batteries by Machine Learning Potential- + Based Molecular Dynamics}}, + journal = {J. Am. Chem. Soc.}, + year = 2024, + volume = 146, + number = 21, + pages = {14566--14575}, + doi = {10.1021/jacs.4c01221}, + abstract = {Due to the increased concern about energy and environmental issues, + significant attention has been paid to the development of large-scale + energy storage devices to facilitate the utilization of clean energy + sources. The redox flow battery (RFB) is one of the most promising + systems. Recently, the high cost of transition-metal complex-based RFB + has promoted the development of aqueous RFBs with redox-active organic + molecules. To expand the working voltage, computational chemistry has + been applied to search for organic molecules with lower or higher + redox potentials. However, redox potential computation based on + implicit solvation models would be challenging due to difficulty in + parametrization when considering the complex solvation of supporting + electrolytes. Besides, although ab initio molecular dynamics (AIMD) + describes the supporting electrolytes with the same level of + electronic structure theory as the redox couple, the application is + impeded by the high computation costs. Recently, machine learning + molecular dynamics (MLMD) has been illustrated to accelerate AIMD by + several orders of magnitude without sacrificing the accuracy. It has + been established that redox potentials can be computed by MLMD with + two separated machine learning potentials (MLPs) for reactant and + product states, which is redundant and inefficient. In this work, an + automated workflow is developed to construct a universal MLP for both + states, which can compute the redox potentials or acidity constants of + redox-active organic molecules more efficiently. Furthermore, the + predicted redox potentials can be evaluated at the hybrid functional + level with much lower costs, which would facilitate the design of + aqueous organic RFBs.}, +} + +@Article{Liu_JApplPhys_2024_v135, + author = {Xiangjun Liu and Baolong Wang and Kun Jia and Quanjie Wang and Di Wang + and Yucheng Xiong}, + title = {{First-principles-based machine learning interatomic potential for + molecular dynamics simulations of 2D lateral MoS2/WS2 heterostructures}}, + journal = {J. Appl. Phys.}, + year = 2024, + volume = 135, + number = 20, + doi = {10.1063/5.0201527}, + abstract = {Understanding the mechanical and + thermodynamic properties of transition-metal dichalcogenides (TMDs) + and their heterostructures is pivotal for advancing the development of + flexible semiconductor devices, and molecular dynamics (MD) simulation + is widely applied to study these properties. However, current + uncertainties persist regarding the efficacy of empirical potentials + in MD simulations to accurately describe the intricate performance of + complex interfaces within heterostructures. This study addresses these + challenges by developing an interatomic potential based on deep neural + networks and first-principles calculations. Specifically focusing on + MoS2/WS2 heterostructures, our approach aims to predict Young's + modulus and thermal conductivities. The potential's effectiveness is + demonstrated through the validation of structural features, mechanical + properties, and thermodynamic characteristics, revealing close + alignment with values derived from first-principles calculations. A + noteworthy finding is the substantial influence of the load direction + on Young's modulus of heterostructures. Furthermore, our results + highlight that the interfacial thermal conductance of the MoS2/WS2 + heterostructures is considerably larger than that of graphene-based + interfaces. The potential developed in this work facilitates large- + scale material simulations, bridging the gap with first-principles + calculations. Notably, it outperforms empirical potentials under + interface conditions, establishing its significant competitiveness in + simulation computations. Our approach not only contributes to a deeper + understanding of TMDs and heterostructures but also presents a robust + tool for the simulation of their mechanical and thermal behaviors, + paving the way for advancements in flexible semiconductor device + manufacturing.}, +} + +@Article{Yokaichiya_JChemPhys_2024_v160_p204108, + author = {Tomoko Yokaichiya and Tatsushi Ikeda and Koki Muraoka and Akira + Nakayama}, + title = {{On-the-fly kinetic Monte Carlo simulations with neural network + potentials for surface diffusion and reaction}}, + journal = {J. Chem. Phys.}, + year = 2024, + volume = 160, + number = 20, + pages = 204108, + doi = {10.1063/5.0199240}, + abstract = {We develop an adaptive scheme in the kinetic Monte Carlo simulations, + where the adsorption and activation energies of all elementary steps, + including the effects of other adsorbates, are evaluated {''}on-the- + fly{''} by employing the neural network potentials. The configurations + and energies evaluated during the simulations are stored for reuse + when the same configurations are sampled in a later step. The present + scheme is applied to hydrogen adsorption and diffusion on the Pd(111) + and Pt(111) surfaces and the CO oxidation reaction on the Pt(111) + surface. The effects of interactions between adsorbates, i.e., + adsorbate-adsorbate lateral interactions, are examined in detail by + comparing the simulations without considering lateral interactions. + This study demonstrates the importance of lateral interactions in + surface diffusion and reactions and the potential of our scheme for + applications in a wide variety of heterogeneous catalytic reactions.}, +} + +@Article{David_JAmChemSoc_2024_v146_p14213, + author = {Rolf David and I{\~n}aki Tu{\~n}{\'o}n and Damien Laage}, + title = {{Competing Reaction Mechanisms of Peptide Bond Formation in Water + Revealed by Deep Potential Molecular Dynamics and Path Sampling}}, + journal = {J. Am. Chem. Soc.}, + year = 2024, + volume = 146, + number = 20, + pages = {14213--14224}, + doi = {10.1021/jacs.4c03445}, + abstract = {The formation of an amide bond is an essential step in the synthesis + of materials and drugs, and in the assembly of amino acids to form + peptides. The mechanism of this reaction has been studied extensively, + in particular to understand how it can be catalyzed, but a + representation capable of explaining all the experimental data is + still lacking. Numerical simulation should provide the necessary + molecular description, but the solvent involvement poses a number of + challenges. Here, we combine the efficiency and accuracy of neural + network potential-based reactive molecular dynamics with the extensive + and unbiased exploration of reaction pathways provided by transition + path sampling. Using microsecond-scale simulations at the density + functional theory level, we show that this method reveals the presence + of two competing distinct mechanisms for peptide bond formation + between alanine esters in aqueous solution. We describe how both + reaction pathways, via a general base catalysis mechanism and via + direct cleavage of the tetrahedral intermediate respectively, change + with pH. This result contrasts with the conventional mechanism + involving a single pathway in which only the barrier heights are + affected by pH. We show that this new proposal involving two competing + mechanisms is consistent with the experimental data, and we discuss + the implications for peptide bond formation under prebiotic conditions + and in the ribosome. Our work shows that integrating deep potential + molecular dynamics with path sampling provides a powerful approach for + exploring complex chemical mechanisms.}, +} + +@Article{Que_JChemPhys_2024_v160_p194710, + author = {Zhi-Xiong Que and Shu-Zong Li and Bo Huang and Zhi-Xiong Yang and Wei- + Bing Zhang}, + title = {{Ultra-flat bands at large twist angles in group-V twisted bilayer + materials}}, + journal = {J. Chem. Phys.}, + year = 2024, + volume = 160, + number = 19, + pages = 194710, + doi = {10.1063/5.0197757}, + abstract = {Flat bands in 2D twisted materials are key to the realization of + correlation-related exotic phenomena. However, a flat band often was + achieved in the large system with a very small twist angle, which + enormously increases the computational and experimental complexity. In + this work, we proposed group-V twisted bilayer materials, including P, + As, and Sb in the {\ensuremath{\beta}} phase with large twist angles. + The band structure of twisted bilayer materials up to 2524 atoms has + been investigated by a deep learning method DeepH, which significantly + reduces the computational time. Our results show that the bandgap and + the flat bandwidth of twisted bilayer {\ensuremath{\beta}}-P, + {\ensuremath{\beta}}-As, and {\ensuremath{\beta}}-Sb reduce gradually + with the decreasing of twist angle, and the ultra-flat band with + bandwidth approaching 0{~}eV is achieved. Interestingly, we found that + a twist angle of 9.43{\textdegree} is sufficient to achieve the band + flatness for {\ensuremath{\beta}}-As comparable to that of twist + bilayer graphene at the magic angle of 1.08{\textdegree}. Moreover, we + also find that the bandgap reduces with decreasing interlayer distance + while the flat band is still preserved, which suggests interlayer + distance as an effective routine to tune the bandgap of flat band + systems. Our research provides a feasible platform for exploring + physical phenomena related to flat bands in twisted layered 2D + materials.}, +} + +@Article{Guo_JChemPhys_2024_v160_p174313, + author = {Longfei Guo and Shuang Shan and Xiaoqing Liu and Wanxuan Zhang and + Peng Xu and Fanzhe Ma and Zhen Li and Chongyang Wang and Junpeng Wang + and Fuyi Chen}, + title = {{Revealing the reconstruction mechanism of AgPd nanoalloys under + fluorination based on a multiscale deep learning potential}}, + journal = {J. Chem. Phys.}, + year = 2024, + volume = 160, + number = 17, + pages = 174313, + doi = {10.1063/5.0205616}, + abstract = {The design of heterogeneous catalysts generally involves optimizing + the reactivity descriptor of adsorption energy, which is inevitably + governed by the structure of surface-active sites. A prerequisite for + understanding the structure-properties relationship is the precise + identification of real surface-active site structures, rather than + relying on conceived structures derived from bulk alloy properties. + However, it remains a formidable challenge due to the dynamic nature + of nanoalloys during catalytic reactions and the lack of accurate and + efficient interatomic potentials for simulations. Herein, a + generalizable deep-learning potential for the Ag-Pd-F system is + developed based on a dataset encompassing the bulk, surface, + nanocluster, amorphous, and point defected configurations with diverse + compositions to achieve a comprehensive description of interatomic + interactions, facilitating precise prediction of adsorption energy, + surface energy, formation energy, and diffusion energy barrier and is + utilized to investigate the structural evolutions of AgPd nanoalloys + during fluorination. The structural evolutions involve the inward + diffusion of F, the outward diffusion of Ag in Ag@Pd nanoalloys, the + formation of surface AgFx species in mixed and Janus AgPd nanoalloys, + and the shape deformation from cuboctahedron to sphere in Ag and Pd@Ag + nanoalloys. Moreover, the effects of atomic diffusion and dislocation + formation and migration on the reconstructing pathway of nanoalloys + are highlighted. It is demonstrated that the stress relaxation upon F + adsorption serves as the intrinsic driving factor governing the + surface reconstruction of AgPd nanoalloys.}, +} + +@Article{Omranpour_JChemPhys_2024_v160_p170901, + author = {Amir Omranpour and Pablo {\{}Montero De Hijes{\}} and J{\"o}rg Behler + and Christoph Dellago}, + title = {{Perspective: Atomistic simulations of water and aqueous systems with + machine learning potentials}}, + journal = {J. Chem. Phys.}, + year = 2024, + volume = 160, + number = 17, + pages = 170901, + doi = {10.1063/5.0201241}, + abstract = {As the most important solvent, water has been at the center of + interest since the advent of computer simulations. While early + molecular dynamics and Monte Carlo simulations had to make use of + simple model potentials to describe the atomic interactions, accurate + ab{~}initio molecular dynamics simulations relying on the first- + principles calculation of the energies and forces have opened the way + to predictive simulations of aqueous systems. Still, these simulations + are very demanding, which prevents the study of complex systems and + their properties. Modern machine learning potentials (MLPs) have now + reached a mature state, allowing us to overcome these limitations by + combining the high accuracy of electronic structure calculations with + the efficiency of empirical force fields. In this Perspective, we give + a concise overview about the progress made in the simulation of water + and aqueous systems employing MLPs, starting from early work on free + molecules and clusters via bulk liquid water to electrolyte solutions + and solid-liquid interfaces.}, +} + +@Article{Zhang_AcsMaterLett_2024_v6_p1849, + author = {Dexin Zhang and Yiwei You and Fulun Wu and Xinrui Cao and Tie-Yu + L{\"u} and Yang Sun and Zi-Zhong Zhu and Shunqing Wu}, + title = {{Exploring the Relationship between Composition and Li-Ion Conductivity + in the Amorphous Li{\textendash}La{\textendash}Zr{\textendash}O System}}, + journal = {Acs Mater. Lett,}, + year = 2024, + volume = 6, + number = 5, + pages = {1849--1855}, + doi = {10.1021/acsmaterialslett.3c01558}, +} + +@Article{Shi_JPhysChemA_2024_v128_p3449, + author = {Zhiyu Shi and Aditya Dilip Lele and Ahren W. Jasper and Stephen J. + Klippenstein and Yiguang Ju}, + title = {{Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab + Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data}}, + journal = {J. Phys. Chem., A}, + year = 2024, + volume = 128, + number = 17, + pages = {3449--3457}, + doi = {10.1021/acs.jpca.4c00750}, + abstract = {Machine learning (ML) provides a great opportunity for the + construction of models with improved accuracy in classical molecular + dynamics (MD). However, the accuracy of a ML trained model is limited + by the quality and quantity of the training data. Generating large + sets of accurate ab initio training data can require significant + computational resources. Furthermore, inconsistent or incompatible + data with different accuracies obtained using different methods may + lead to biased or unreliable ML models that do not accurately + represent the underlying physics. Recently, transfer learning showed + its potential for avoiding these problems as well as for improving the + accuracy, efficiency, and generalization of ML models using + multifidelity data. In this work, ab initio trained ML-based MD (aML- + MD) models are developed through transfer learning using DFT and + multireference data from multiple sources with varying accuracy within + the Deep Potential MD framework. The accuracy of the force field is + demonstrated by calculating rate constants for the H + HO2 + {\textrightarrow} H2 + 3O2 reaction using quasi-classical + trajectories. We show that the aML-MD model with transfer learning can + accurately predict the rate constants while reducing the computational + cost by more than five times compared to the use of more expensive + quantum chemistry training data sets. Hence, the aML-MD model with + transfer learning shows great potential in using multifidelity data to + reduce the computational cost involved in generating the training set + for these potentials.}, +} + +@Article{Selvaraj_JElectrochemSoc_2024_v171_p50544, + author = {Selva Chandrasekaran Selvaraj and Volodymyr Koverga and Anh T. Ngo}, + title = {{Exploring Li-Ion Transport Properties of Li}}3TiCl}}6: A Machine + Learning Molecular Dynamics Study}}, + journal = {J. Electrochem., Soc,}, + year = 2024, + volume = 171, + number = 5, + pages = 50544, + doi = {10.1149/1945-7111/ad4ac9}, + abstract = {We performed large-scale + molecular dynamics simulations based on a machine-learning force field + (MLFF) to investigate the Li-ion transport mechanism in cation- + disordered Li3< + /jats:sub>TiCl6 + cathode at six different + temperatures, ranging from 25{\textdegree}C to 100{\textdegree}C. In + this work, deep neural network method and data generated by ab + {\ensuremath{-}} initio molecular dynamics (AIMD) simulations were + deployed to build a high-fidelity MLFF. Radial distribution functions, + Li-ion mean square displacements (MSD), diffusion coefficients, ionic + conductivity, activation energy, and crystallographic direction- + dependent migration barriers were calculated and compared with + corresponding AIMD and experimental data to benchmark the accuracy of + the MLFF. From MSD analysis, we captured both the self and distinct + parts of Li-ion dynamics. The latter reveals that the Li-ions are + involved in anti-correlation motion that was rarely reported for + solid-state materials. Similarly, the self and distinct parts of Li- + ion dynamics were used to determine Haven{\textquoteright}s ratio to + describe the Li-ion transport mechanism in Li}}3TiCl{\ensurem + ath{<}}jats:sub>6}}. Obtained trajectory from molecular dynamics infers that the Li- + ion transportation is mainly through interstitial hopping which was + confirmed by intra- and inter-layer Li-ion displacement with respect + to simulation time. Ionic conductivity (1.06 mS/cm) and activation + energy (0.29eV) calculated by our simulation are highly comparable + with that of experimental values. Overall, the combination of machine- + learning methods and AIMD simulations explains the intricate + electrochemical properties of the Li}}3TiCl6 + cathode with remarkably reduced computational time. Thus, our work + strongly suggests that the deep neural network-based MLFF could be a + promising method for large-scale complex + materials.}, +} + +@Article{Li_PhysRevB_2024_v109_p184108, + author = {Zhi Li and Sandro Scandolo}, + title = {{Deep-learning interatomic potential for iron at extreme conditions}}, + journal = {Phys. Rev. B}, + year = 2024, + volume = 109, + number = 18, + pages = 184108, + doi = {10.1103/PhysRevB.109.184108}, +} + +@Article{Shi_PhysRevB_2024_v109_p174104, + author = {Yubai Shi and Ri He and Bingwen Zhang and Zhicheng Zhong}, + title = {{Revisiting the phase diagram and piezoelectricity of lead zirconate + titanate from first principles}}, + journal = {Phys. Rev. B}, + year = 2024, + volume = 109, + number = 17, + pages = 174104, + doi = {10.1103/PhysRevB.109.174104}, +} + +@Article{Zhang_PhysRevB_2024_v109_p174106, + author = {Xi Zhang and Yu-Tian Zhang and Yun-Peng Wang and Shiyu Li and Shixuan + Du and Yu-Yang Zhang and Sokrates T. Pantelides}, + title = {{Structural and mechanical properties of monolayer amorphous carbon and + boron nitride}}, + journal = {Phys. Rev. B}, + year = 2024, + volume = 109, + number = 17, + pages = 174106, + doi = {10.1103/PhysRevB.109.174106}, + abstract = {Amorphous materials exhibit various characteristics that are not + featured by crystals and can sometimes be tuned by their degree of + disorder (DOD). Here, we report results on the mechanical properties + of monolayer amorphous carbon (MAC) and monolayer amorphous boron + nitride (maBN) with different DOD. The pertinent structures are + obtained by kinetic-Monte-Carlo (kMC) simulations using machine- + learning potentials (MLP) with density-functional-theory (DFT)-level + accuracy. An intuitive order parameter, namely the areal fraction Fx + occupied by crystallites within the continuous random network, is + proposed to describe the DOD. We find that Fx captures the essence of + the DOD: Samples with the same Fx but different sizes and + distributions of crystallites have virtually identical radial + distributions functions as well as bond-length and bond-angle + distributions. Furthermore, by simulating the fracture process with + molecular dynamics, we found that the mechanical responses of MAC and + maBN before fracture are solely determined by Fx and are insensitive + to the sizes and specific arrangements of the crystallites. The + behavior of cracks in the two materials is analyzed and found to + mainly propagate in meandering paths in the CRN region and to be + influenced by crystallites in distinct ways that toughen the material. + The present results reveal the relation between structure and + mechanical properties in amorphous monolayers and may provide a + universal toughening strategy for 2D materials.}, +} + +@Article{Shi_ExtremMechLett_2024_v68_p102151, + author = {Pengjie Shi and Shizhe Feng and Zhiping Xu}, + title = {{Non-equilibrium nature of fracture determines the crack paths}}, + journal = {Extrem. Mech. Lett.}, + year = 2024, + volume = 68, + pages = 102151, + doi = {10.1016/j.eml.2024.102151}, +} + +@Article{Xiao_BioresourTechnol_2024_v399_p130590, + author = {Yuqin Xiao and Yuxin Yan and Hainam Do and Richard Rankin and Haitao + Zhao and Ping Qian and Keke Song and Tao Wu and Cheng Heng Pang}, + title = {{Understanding cellulose pyrolysis via ab initio deep learning + potential field}}, + journal = {Bioresour. Technol.}, + year = 2024, + volume = 399, + pages = 130590, + doi = {10.1016/j.biortech.2024.130590}, + abstract = {Comprehensive and dynamic studies of cellulose pyrolysis reaction + mechanisms are crucial in designing experiments and processes with + enhanced safety, efficiency, and sustainability. The details of the + pyrolysis mechanism are not readily available from experiments but can + be better described via molecular dynamics (MD) simulations. However, + the large size of cellulose molecules challenges accurate ab initio MD + simulations, while existing reactive force field parameters lack + precision. In this work, precise ab initio deep learning potentials + field (DPLF) are developed and applied in MD simulations to facilitate + the study of cellulose pyrolysis mechanisms. The formation mechanism + and production rate of both valuable and greenhouse products from + cellulose at temperatures larger than 1073{~}K are comprehensively + described. This study underscores the critical role of advanced + simulation techniques, particularly DLPF, in achieving efficient and + accurate understanding of cellulose pyrolysis mechanisms, thus + promoting wider industrial applications.}, +} + +@Article{Mirchi_PhysChemChemPhys_2024_v26_p14216, + author = {Pedram Mirchi and Christophe Adessi and Samy Merabia and Ali Rajabpour}, + title = {{Lattice thermal conductivity and mechanical properties of the single- + layer penta-NiN2 explored by a deep-learning interatomic potential}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2024, + volume = 26, + number = 19, + pages = {14216--14227}, + doi = {10.1039/d4cp00997e}, + abstract = {Penta-NiN2, a novel pentagonal 2D sheet with potential nanoelectronic + applications, is investigated in terms of its lattice thermal + conductivity, stability, and mechanical behavior. A deep learning + interatomic potential (DLP) is firstly generated from ab initio + molecular dynamics (AIMD) data and then utilized for classical + molecular dynamics simulations. The DLP's accuracy is verified, + showing strong agreement with AIMD results. The dependence of thermal + conductivity on size, temperature, and tensile strain, reveals + important insights into the material's thermal properties. + Additionally, the mechanical response of penta-NiN2 under uniaxial + loading is examined, yielding a Young's modulus of approximately 368 + GPa. The influence of vacancy defects on mechanical properties is + analyzed, demonstrating a significant reduction in modulus, fracture + stress, and ultimate strength. This study also investigates the + influence of strain on phonon dispersion relations and phonon group + velocity in penta-NiN2, shedding light on how alterations in the + atomic lattice affect the phonon dynamics and, consequently, impact + the thermal conductivity. This investigation showcases the ability of + deep learning-based interatomic potentials in studying the properties + of 2D penta-NiN2.}, +} + +@Article{Dong_JApplPhys_2024_v135, + author = {Haikuan Dong and Yongbo Shi and Penghua Ying and Ke Xu and Ting Liang + and Yanzhou Wang and Zezhu Zeng and Xin Wu and Wenjiang Zhou and + Shiyun Xiong and Shunda Chen and Zheyong Fan}, + title = {{Molecular dynamics simulations of heat transport using machine-learned + potentials: A mini-review and tutorial on GPUMD with neuroevolution + potentials}}, + journal = {J. Appl. Phys.}, + year = 2024, + volume = 135, + number = 16, + doi = {10.1063/5.0200833}, + abstract = {Molecular dynamics (MD) + simulations play an important role in understanding and engineering + heat transport properties of complex materials. An essential + requirement for reliably predicting heat transport properties is the + use of accurate and efficient interatomic potentials. Recently, + machine-learned potentials (MLPs) have shown great promise in + providing the required accuracy for a broad range of materials. In + this mini-review and tutorial, we delve into the fundamentals of heat + transport, explore pertinent MD simulation methods, and survey the + applications of MLPs in MD simulations of heat transport. Furthermore, + we provide a step-by-step tutorial on developing MLPs for highly + efficient and predictive heat transport simulations, utilizing the + neuroevolution potentials as implemented in the GPUMD package. Our aim + with this mini-review and tutorial is to empower researchers with + valuable insights into cutting-edge methodologies that can + significantly enhance the accuracy and efficiency of MD simulations + for heat transport studies.}, +} + +@Article{Balyakin_ComputMaterSci_2024_v239_p112979, + author = {I.A. Balyakin and M.I. Vlasov and S.V. Pershina and D.M. Tsymbarenko + and A.A. Rempel}, + title = {{Neural network molecular dynamics study of LiGe2(PO4)3: Investigation + of structure}}, + journal = {Comput. Mater. Sci.}, + year = 2024, + volume = 239, + pages = 112979, + doi = {10.1016/j.commatsci.2024.112979}, +} + +@Article{Ghaffari_ComputMaterSci_2024_v239_p112983, + author = {Kimia Ghaffari and Salil Bavdekar and Douglas E. Spearot and Ghatu + Subhash}, + title = {{Validation workflow for machine learning interatomic potentials for + complex ceramics}}, + journal = {Comput. Mater. Sci.}, + year = 2024, + volume = 239, + pages = 112983, + doi = {10.1016/j.commatsci.2024.112983}, +} + +@Article{Zhu_ComputMaterSci_2024_v239_p112966, + author = {Chang-sheng Zhu and Wen-jing Dong and Zi-hao Gao and Li-jun Wang and + Guang-zhao Li}, + title = {{Deep Potential fitting and mechanical properties study of MgAlSi alloy}}, + journal = {Comput. Mater. Sci.}, + year = 2024, + volume = 239, + pages = 112966, + doi = {10.1016/j.commatsci.2024.112966}, +} + +@Article{Zhu_JPhysChemLett_2024_v15_p4024, + author = {Da Zhu and Li Sheng and Taiping Hu and Sian Chen and Mengchao Shi and + Haiming Hua and Kai Yang and Jianlong Wang and Yaping Tang and + Xiangming He and Hong Xu}, + title = {{Investigation of the Degradation of LiPF6- in Polar Solvents through + Deep Potential Molecular Dynamics}}, + journal = {J. Phys. Chem. Lett.}, + year = 2024, + volume = 15, + number = 15, + pages = {4024--4030}, + doi = {10.1021/acs.jpclett.4c00575}, + abstract = {The nonaqueous electrolyte based on lithium hexafluorophosphate + (LiPF6) is the dominant liquid electrolyte in lithium-ion batteries + (LIBs). However, trace protic impurities, including H3O+, alcohols, + and hydrofluoric acid (HF), can trigger a series of side reactions + that lead to rapid capacity fading in high energy density LIBs. It is + worth noting that this degradation process is highly dependent on the + polarity of the solvents. In this work, a deep potential (DP) model is + trained with a certain commercial electrolyte formula through a + machine learning method. H3O+ is anchored with polar solvents, making + it difficult to approach the PF6-, and suppressing the degradation + process quickly at room temperature. Control experiments and + simulations at different temperatures or concentrations are also + performed to verify it. This work proposes a precise model to describe + the solvation structure quantitatively and offers a new perspective on + the degradation mechanism of PF6- in polar solvents.}, +} + +@Article{Zills_JPhysChemB_2024_v128_p3662, + author = {Fabian Zills and Moritz Ren{\'e} Sch{\"a}fer and Nico Segreto and + Johannes K{\"a}stner and Christian Holm and Samuel Tovey}, + title = {{Collaboration on Machine-Learned Potentials with IPSuite: A Modular + Framework for Learning-on-the-Fly}}, + journal = {J. Phys. Chem., B}, + year = 2024, + volume = 128, + number = 15, + pages = {3662--3676}, + doi = {10.1021/acs.jpcb.3c07187}, + abstract = {The field of machine learning potentials has experienced a rapid surge + in progress, thanks to advances in machine learning theory, + algorithms, and hardware capabilities. While the underlying methods + are continuously evolving, the infrastructure for their deployment has + lagged. The community, due to these rapid developments, frequently + finds itself split into groups built around different implementations + of machine-learned potentials. In this work, we introduce IPSuite, a + Python-driven software package designed to connect different methods + and algorithms from the comprehensive field of machine-learned + potentials into a single platform while also providing a collaborative + infrastructure, helping ensure reproducibility. Furthermore, the data + management infrastructure of the IPSuite code enables simple model + sharing and deployment in simulations. Currently, IPSuite supports six + state-of-the-art machine learning approaches for the fitting of + interatomic potentials as well as a variety of methods for the + selection of training data, running of ab initio calculations, + learning-on-the-fly strategies, model evaluation, and simulation + deployment.}, +} + +@Article{Zhou_AcsApplMaterInterfaces_2024_v16_p18874, + author = {Rui Zhou and Kun Luo and Steve W. Martin and Qi An}, + title = {{Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic + Conductivity via Machine Learning Force Field Simulations}}, + journal = {Acs Appl. Mater. Interfaces}, + year = 2024, + volume = 16, + number = 15, + pages = {18874--18887}, + doi = {10.1021/acsami.4c00618}, + abstract = {Sulfide-based solid electrolytes (SEs) are important for advancing + all-solid-state batteries (ASSBs), primarily due to their high ionic + conductivities and robust mechanical stability. Glassy SEs (GSEs) + comprising mixed Si and P glass formers are particularly promising for + their synthesis process and their ability to prevent lithium dendrite + growth. However, to date, the complexity of their glassy structures + hinders a complete understanding of the relationships between their + structures and properties. This study introduces a new machine + learning force field (ML-FF) tailored for lithium sulfide-based GSEs, + enabling the exploration of their structural characteristics, + mechanical properties, and lithium ionic conductivities. Using + molecular dynamic (MD) simulations with this ML-FF, we explore the + glass structures in varying compositions, including binary Li2S-SiS2 + and Li2S-P2S5 as well as ternary Li2S-SiS2-P2S5. Our simulations + yielded consistent results in terms of density, elastic modulus, + radial distribution functions, and neutron structure factors compared + to DFT and experimental work. Our findings reveal distinct local + environments for Si and P within these glasses, with most Si atoms in + edge-sharing configurations in Li2S-SiS2 and a mix of corner- and + edge-sharing tetrahedra in the ternary Li2S-SiS2-P2S5 composition. For + lithium ionic conductivity at 300 K, the 50Li2S-50SiS2 glass displayed + the lowest conductivity at 2.1 mS/cm, while the 75Li2S-25P2S5 + composition exhibited the highest conductivity at 3.6 mS/cm. The + ternary glass showed a conductivity of 2.6 mS/cm, sitting between the + two. Moreover, an in-depth analysis of lithium ion diffusion over the + MD trajectory in the ternary glass demonstrated a significant + correlation between diffusion pathways and the rotational dynamics of + nearby SiS4 or PS4 tetrahedra. The ML-FF developed in this study + provides an important tool for exploring a broad spectrum of solid- + state and mixed former sulfide-based electrolytes.}, +} + +@Article{Zhang_InorgChem_2024_v63_p6743, + author = {Rongyu Zhang and Shifeng Xu and Liyan Wang and Chuanyun Wang and + Yongjun Zhou and Zhe L{\"u} and Wenbo Li and Dan Xu and Sai Wang and + Xu Yang}, + title = {{Theoretical Study on Ion Diffusion Mechanism in W-Doped K3SbS4 as + Solid-State Electrolyte for K-Ion Batteries}}, + journal = {Inorg. Chem.}, + year = 2024, + volume = 63, + number = 15, + pages = {6743--6751}, + doi = {10.1021/acs.inorgchem.4c00074}, + abstract = {The development of a solid-state electrolyte (SSE) is crucial for + overcoming the side reactions of metal potassium anodes and advancing + the progress of K-ion batteries (KIBs). Exploring the diffusion + mechanism of the K ion in SSE is important for deepening our + understanding and promoting its development. In this study, we + conducted static calculations and utilized deep potential molecular + dynamics (DeepMD) to investigate the behavior of cubic K3SbS4. The + original K3SbS4 exhibited poor ionic conductivity, but we discovered + that introducing heterovalent tungsten doping created vacancies, which + significantly reduced the activation energy to 0.12 eV and enhanced + the ionic conductivity to 1.80 {\texttimes} 10-2 S/cm. The diffusion + of K-ions in K3SbS4 primarily occurs through the exchange of positions + with K vacancies. This research provides insights into the design of + SSE with high ionic conductivity. Furthermore, it highlights the + effectiveness of DeepMD as a powerful tool for studying the SSE.}, +} + +@Article{Woo_MaterLett_2024_v361_p136114, + author = {Sung Hun Woo and Hyun Joo Yang and Yongseon Kim}, + title = {{Investigation of the effect of off-stoichiometric composition on + oxygen transport in layered perovskite materials for SOFC cathode}}, + journal = {Mater. Lett.}, + year = 2024, + volume = 361, + pages = 136114, + doi = {10.1016/j.matlet.2024.136114}, +} + +@Article{Feng_ChemEngSci_2024_v288_p119836, + author = {Taixi Feng and Bo Yang and Jia Zhao and Guimin Lu}, + title = {{Elucidating the local structure and properties of molten Na2CO3-K2CO3 + salts using Machine Learning-Driven molecular dynamics}}, + journal = {Chem. Eng. Sci.}, + year = 2024, + volume = 288, + pages = 119836, + doi = {10.1016/j.ces.2024.119836}, +} + +@Article{Zhang_CeramInt_2024_v50_p13740, + author = {Hanchao Zhang and Guoliang Ren and Peng Jia and Xiaofeng Zhao and Na + Ni}, + title = {{Development of machine learning force field for thermal conductivity + analysis in MoAlB: Insights into anisotropic heat transfer mechanisms}}, + journal = {Ceram. Int.}, + year = 2024, + volume = 50, + number = 8, + pages = {13740--13749}, + doi = {10.1016/j.ceramint.2024.01.288}, +} + +@Article{Pan_JComputChem_2024_v45_p638, + author = {Xiaoliang Pan and Ryan Snyder and Jia-Ning Wang and Chance Lander and + Carly Wickizer and Richard Van and Andrew Chesney and Yuanfei Xue and + Yuezhi Mao and Ye Mei and Jingzhi Pu and Yihan Shao}, + title = {{Training machine learning potentials for reactive systems: A Colab + tutorial on basic models}}, + journal = {J. Comput. Chem.}, + year = 2024, + volume = 45, + number = 10, + pages = {638--647}, + doi = {10.1002/jcc.27269}, + abstract = {In the last several years, there has been a surge in the development + of machine learning potential (MLP) models for describing molecular + systems. We are interested in a particular area of this field - the + training of system-specific MLPs for reactive systems - with the goal + of using these MLPs to accelerate free energy simulations of chemical + and enzyme reactions. To help new members in our labs become familiar + with the basic techniques, we have put together a self-guided Colab + tutorial (https://cc-ats.github.io/mlp{\_}tutorial/), which we expect + to be also useful to other young researchers in the community. Our + tutorial begins with the introduction of simple feedforward neural + network (FNN) and kernel-based (using Gaussian process regression, + GPR) models by fitting the two-dimensional M{\"u}ller-Brown potential. + Subsequently, two simple descriptors are presented for extracting + features of molecular systems: symmetry functions (including the ANI + variant) and embedding neural networks (such as DeepPot-SE). Lastly, + these features will be fed into FNN and GPR models to reproduce the + energies and forces for the molecular configurations in a Claisen + rearrangement reaction.}, +} + +@Article{Chen_JApplPhys_2024_v135, + author = {Rongkun Chen and Yu Tian and Jiayi Cao and Weina Ren and Shiqian Hu + and Chunhua Zeng}, + title = {{Unified deep learning network for enhanced accuracy in predicting + thermal conductivity of bilayer graphene, hexagonal boron nitride, and + their heterostructures}}, + journal = {J. Appl. Phys.}, + year = 2024, + volume = 135, + number = 14, + doi = {10.1063/5.0201698}, + abstract = {In this research, we utilized + density functional theory (DFT) computations to perform ab initio + molecular dynamics simulations and static calculations on graphene, + hexagonal boron nitride, and their heterostructures, subjecting them + to strains, perturbations, twist angles, and defects. The gathered + energy, force, and virial information informed the creation of a + training set comprising 1253 structures. Employing the Neural + Evolutionary Potential framework integrated into Graphics Processing + Units Molecular Dynamics, we fitted a machine learning potential (MLP) + that closely mirrored the DFT potential energy surface. Rigorous + validation of lattice constants and phonon dispersion relations + confirmed the precision and dependability of the MLP, establishing a + solid foundation for subsequent thermal transport investigations. A + further analysis of the impact of twist angles uncovered a significant + reduction in thermal conductivity, particularly notable in + heterostructures with a decline exceeding 35{\%}. The reduction in + thermal conductivity primarily stems from the twist angle-induced + softening of phonon modes and the accompanying increase in phonon + scattering rates, which intensifies anharmonic interactions among + phonons. Our study underscores the efficacy of the MLP in delineating + the thermal transport attributes of two-dimensional materials and + their heterostructures, while also elucidating the micro-mechanisms + behind the influence of the twist angle on thermal conductivity, + offering fresh perspectives for the design of advanced thermal + management materials.}, +} + +@Article{Zhai_JChemPhys_2024_v160_p144501, + author = {Yaoguang Zhai and Richa Rashmi and Etienne Palos and Francesco Paesani}, + title = {{Many-body interactions and deep neural network potentials for water}}, + journal = {J. Chem. Phys.}, + year = 2024, + volume = 160, + number = 14, + pages = 144501, + doi = {10.1063/5.0203682}, + abstract = {We present a detailed assessment of deep neural network potentials + developed within the Deep Potential Molecular Dynamics (DeePMD) + framework and trained on the MB-pol data-driven many-body potential + energy function. Specific focus is directed at the ability of DeePMD- + based potentials to correctly reproduce the accuracy of MB-pol across + various water systems. Analyses of bulk and interfacial properties as + well as many-body interactions characteristic of water elucidate + inherent limitations in the transferability and predictive accuracy of + DeePMD-based potentials. These limitations can be traced back to an + incomplete implementation of the {''}nearsightedness of electronic + matter{''} principle, which may be common throughout machine learning + potentials that do not include a proper representation of self- + consistently determined long-range electric fields. These findings + provide further support for the {''}short-blanket dilemma{''} faced by + DeePMD-based potentials, highlighting the challenges in achieving a + balance between computational efficiency and a rigorous, physics-based + representation of the properties of water. Finally, we believe that + our study contributes to the ongoing discourse on the development and + application of machine learning models in simulating water systems, + offering insights that could guide future improvements in the field.}, +} + +@Article{Maxson_JPhysChemLett_2024_v15_p3740, + author = {Tristan Maxson and Tibor Szilv{\'a}si}, + title = {{Transferable Water Potentials Using Equivariant Neural Networks}}, + journal = {J. Phys. Chem. Lett.}, + year = 2024, + volume = 15, + number = 14, + pages = {3740--3747}, + doi = {10.1021/acs.jpclett.4c00605}, + abstract = {Machine learning interatomic potentials (MLIPs) have emerged as a + technique that promises quantum theory accuracy for reduced cost. It + has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs + trained on solely liquid water data cannot accurately transfer to the + vapor-liquid equilibrium while recovering the many-body decomposition + (MBD) analysis of gas-phase water clusters. This suggests that MLIPs + do not directly learn the physically correct interactions of water + molecules, limiting transferability. In this work, we show that MLIPs + using equivariant architecture and trained on 3200 liquid water + structures reproduces liquid-phase water properties (e.g., density + within 0.003 g/cm3 between 230 and 365 K), vapor-liquid equilibrium + properties up to 550 K, the MBD analysis of gas-phase water cluster up + to six-body interactions, and the relative energy and the vibrational + density of states of ice phases. We show that potentials developed + using equivariant MLIPs allow transferability for arbitrary phases of + water that remain stable in nanosecond long simulations.}, +} + +@Article{Miyagawa_JMaterChemA_2024_v12_p11344, + author = {Takeru Miyagawa and Namita Krishnan and Manuel Grumet and Christian + Rever{\'o}n Baecker and Waldemar Kaiser and David A. Egger}, + title = {{Accurate description of ion migration in solid-state ion conductors + from machine-learning molecular dynamics}}, + journal = {J. Mater. Chem. A}, + year = 2024, + volume = 12, + number = 19, + pages = {11344--11361}, + doi = {10.1039/d4ta00452c}, + abstract = {Machine-learning molecular + dynamics provides predictions of structural and anharmonic vibrational + properties of solid-state ionic conductors with + ab + initio accuracy. This + opens a path towards rapid design of novel battery + materials.}, +} + +@Article{Ying_AcsNano_2024_v18_p10133, + author = {Penghua Ying and Amir Natan and Oded Hod and Michael Urbakh}, + title = {{Effect of Interlayer Bonding on Superlubric Sliding of Graphene + Contacts: A Machine-Learning Potential Study}}, + journal = {Acs Nano}, + year = 2024, + volume = 18, + number = 14, + pages = {10133--10141}, + doi = {10.1021/acsnano.3c13099}, + abstract = {Surface defects and their mutual interactions are anticipated to + affect the superlubric sliding of incommensurate layered material + interfaces. Atomistic understanding of this phenomenon is limited due + to the high computational cost of ab initio simulations and the + absence of reliable classical force-fields for molecular dynamics + simulations of defected systems. To address this, we present a + machine-learning potential (MLP) for bilayer defected graphene, + utilizing state-of-the-art graph neural networks trained against many- + body dispersion corrected density functional theory calculations under + iterative configuration space exploration. The developed MLP is + utilized to study the impact of interlayer bonding on the friction of + bilayer defected graphene interfaces. While a mild effect on the + sliding dynamics of aligned graphene interfaces is observed, the + friction coefficients of incommensurate graphene interfaces are found + to significantly increase due to interlayer bonding, nearly pushing + the system out of the superlubric regime. The methodology utilized + herein is of general nature and can be adapted to describe other + homogeneous and heterogeneous defected layered material interfaces.}, +} + +@Article{Zhang_ApplPhysLett_2024_v124, + author = {Jingwen Zhang and Junjie Zhang and Guoqiang Bao and Zehan Li and + Xiaobo Li and Te-Huan Liu and Ronggui Yang}, + title = {{Prediction of phonon properties of cubic boron nitride with vacancy + defects and isotopic disorders by using a neural network potential}}, + journal = {Appl. Phys. Lett.}, + year = 2024, + volume = 124, + number = 15, + doi = {10.1063/5.0198431}, + abstract = {Cubic boron nitride (c-BN) is a + promising ultra-wide bandgap semiconductor for high-power electronic + devices. Its thermal conductivity can be substantially modified by + controlling the isotope abundance and by the quality of a single + crystal. Consequently, an understanding of the phonon transport in + c-BN crystals, with both vacancy defects and isotopic disorders at + near-ambient temperatures, is of practical importance. In the present + study, a neural network potential (NNP) for c-BN has been developed, + which has facilitated the investigation of phonon properties under + these circumstances. As a result, the phonon dispersion and the three- + and four-phonon scattering rates that were predicted with this NNP + were in close agreement with those obtained from density-functional + theory (DFT) calculations. The thermal conductivities of the c-BN + crystals were also investigated, with boron (B) vacancies ranging from + 0.0{\%} to 0.6{\%}, by using equilibrium molecular dynamics + simulations based on the Green-Kubo formula. These simulations + accurately capture vacancy-induced phonon softening, localized + vibration modes, and phonon localization effects. As has previously + been experimentally prepared, four isotope-modified c-BN samples were + selected for analyses in the evaluation of the impact of isotopic + disorders. The calculated thermal conductivities aligned well with the + DFT benchmarks. In addition, the present study was extended to include + a c-BN crystal with a natural abundance of B atoms, which also + contained B vacancies. Reasonable thermal conductivities and + vibrational characteristics, within the temperature range of + 250{\textendash}500{\,}K, were then + obtained.}, +} + +@Article{Xu_JPhysChemC_2024_v128_p5697, + author = {Sen Xu and Liling Wu and Yi Fan and Yufeng Liu and Xiongzhi Zeng and + Zhenyu Li}, + title = {{Hydrocarbon Species on the Cu(111) Surface Studied with a Neural + Network Potential}}, + journal = {J. Phys. Chem. C}, + year = 2024, + volume = 128, + number = 13, + pages = {5697--5707}, + doi = {10.1021/acs.jpcc.3c08138}, +} + +@Article{Kobayashi_ChemSci_2024_v15_p6816, + author = {Taro Kobayashi and Tatsushi Ikeda and Akira Nakayama}, + title = {{Long-range proton and hydroxide ion transfer dynamics at the + water/CeO2 interface in the nanosecond regime: reactive molecular + dynamics simulations and kinetic analysis}}, + journal = {Chem. Sci.}, + year = 2024, + volume = 15, + number = 18, + pages = {6816--6832}, + doi = {10.1039/d4sc01422g}, + abstract = {The structural properties, dynamical behaviors, and ion transport + phenomena at the interface between water and cerium oxide are + investigated by reactive molecular dynamics (MD) simulations employing + neural network potentials (NNPs). The NNPs are trained to reproduce + density functional theory (DFT) results, and DFT-based MD (DFT-MD) + simulations with enhanced sampling techniques and refinement schemes + are employed to efficiently and systematically acquire training data + that include diverse hydrogen-bonding configurations caused by proton + hopping events. The water interfaces with two low-index surfaces of + (111) and (110) are explored with these NNPs, and the structure and + long-range proton and hydroxide ion transfer dynamics are examined + with unprecedented system sizes and long simulation times. Various + types of proton hopping events at the interface are categorized and + analyzed in detail. Furthermore, in order to decipher the proton and + hydroxide ion transport phenomena along the surface, a counting + analysis based on the semi-Markov process is formulated and applied to + the MD trajectories to obtain reaction rates by considering the + transport as stochastic jump processes. Through this model, the + coupling between hopping events, vibrational motions, and hydrogen + bond networks at the interface are quantitatively examined, and the + high activity and ion transport phenomena at the water/CeO2 interface + are unequivocally revealed in the nanosecond regime.}, +} + +@Article{Hsing_PhysRevMater_2024_v8_p43806, + author = {Cheng-Rong Hsing and Duc-Long Nguyen and Ching-Ming Wei}, + title = {{Exploring diffusion behavior of superionic materials using machine- + learning interatomic potentials}}, + journal = {Phys, Rev, Mater.}, + year = 2024, + volume = 8, + number = 4, + pages = 43806, + doi = {10.1103/PhysRevMaterials.8.043806}, +} + +@Article{Agrawal_Nanoscale_2024_v16_p8986, + author = {Sraddha Agrawal and Bipeng Wang and Yifan Wu and David Casanova and + Oleg V. Prezhdo}, + title = {{Photocatalytic activity of dual defect modified graphitic carbon + nitride is robust to tautomerism: machine learning assisted ab initio + quantum dynamics}}, + journal = {Nanoscale}, + year = 2024, + volume = 16, + number = 18, + pages = {8986--8995}, + doi = {10.1039/d4nr00606b}, + abstract = {Two-dimensional graphitic carbon nitride (GCN) is a popular metal-free + polymer for sustainable energy applications due to its unique + structure and semiconductor properties. Dopants and defects are used + to tune GCN, and dual defect modified GCN exhibits superior properties + and enhanced photocatalytic efficiency in comparison to pristine or + single defect GCN. We employ a multistep approach combining time- + dependent density functional theory and nonadiabatic molecular + dynamics (NAMD) with machine learning (ML) to investigate coupled + structural and electronic dynamics in GCN over a nanosecond timescale, + comparable to and exceeding the lifetimes of photo-generated charge + carriers and photocatalytic events. Although frequent hydrogen hopping + transitions occur among four tautomeric structures, the electron-hole + separation and recombination processes are only weakly sensitive to + the tautomerism. The charge separated state survives for about 10 ps, + sufficiently long to enable photocatalysis. The employed ML-NAMD + methodology provides insights into rare events that can influence + excited state dynamics in the condensed phase and nanoscale materials + and extends NAMD simulations from pico- to nanoseconds. The ab initio + quantum dynamics simulation provides a detailed atomistic mechanism of + photoinduced evolution of charge carriers in GCN and rationalizes how + GCN remains photo-catalytically active despite its multiple isomeric + and tautomeric forms.}, +} + +@Article{Peng_JgrSolidEarth_2024_v129, + author = {Yihang Peng and Jie Deng}, + title = {{Hydrogen Diffusion in the Lower Mantle Revealed by Machine Learning + Potentials}}, + journal = {Jgr Solid Earth}, + year = 2024, + volume = 129, + number = 4, + doi = {10.1029/2023JB028333}, + abstract = {AbstractHydrogen + may be incorporated into nominally anhydrous minerals including + bridgmanite and post{-}perovskite as defects, making the Earth's deep + mantle a potentially significant water reservoir. The diffusion of + hydrogen and its contribution to the electrical conductivity in the + lower mantle are rarely explored and remain largely unconstrained. + Here we calculate hydrogen diffusivity in hydrous bridgmanite and + post{-}perovskite, using molecular dynamics simulations driven by + machine learning potentials of ab initio quality. Our findings reveal + that hydrogen diffusivity significantly increases with increasing + temperature and decreasing pressure, and is considerably sensitive to + hydrogen incorporation mechanism. Among the four defect mechanisms + examined, (Mg{~}+{~}2H)Si{\ens + uremath{<}}/jats:sub> and (Al{~}+{~}H)Si + show similar patterns and yield the highest hydrogen diffusivity. + Hydrogen diffusion is generally faster in post{-}perovskite than in + bridgmanite, and these two phases exhibit distinct diffusion + anisotropies. Overall, hydrogen diffusion is slow on geological time + scales and may result in heterogeneous water distribution in the lower + mantle. Additionally, the proton conductivity of bridgmanite for (Mg{~ + }+{~}2H)Si and (Al{~}+{~}H)}}Si defects aligns + with the same order of magnitude of lower mantle conductivity, + suggesting that the water distribution in the lower mantle may be + inferred by examining the heterogeneity of electrical + conductivity.}, +} + +@Article{Du_NatlSciRev_2024_v11_pnwae023, + author = {Tao Du and Shanwu Li and Sudheer Ganisetti and Mathieu Bauchy and + Yuanzheng Yue and Morten M. Smedskjaer}, + title = {{Deciphering the controlling factors for phase transitions in zeolitic + imidazolate frameworks}}, + journal = {Natl. Sci. Rev.}, + year = 2024, + volume = 11, + number = 4, + pages = {nwae023}, + doi = {10.1093/nsr/nwae023}, + abstract = {Zeolitic imidazolate frameworks (ZIFs) feature complex phase + transitions, including polymorphism, melting, vitrification, and + polyamorphism. Experimentally probing their structural evolution + during transitions involving amorphous phases is a significant + challenge, especially at the medium-range length scale. To overcome + this challenge, here we first train a deep learning-based force field + to identify the structural characteristics of both crystalline and + non-crystalline ZIF phases. This allows us to reproduce the structural + evolution trend during the melting of crystals and formation of ZIF + glasses at various length scales with an accuracy comparable to that + of ab initio molecular dynamics, yet at a much lower computational + cost. Based on this approach, we propose a new structural descriptor, + namely, the ring orientation index, to capture the propensity for + crystallization of ZIF-4 (Zn(Im)2, Im{~}={~}C3H3N2-) glasses, as well + as for the formation of ZIF-zni (Zn(Im)2) out of the high-density + amorphous phase. This crystal formation process is a result of the + reorientation of imidazole rings by sacrificing the order of the + structure around the zinc-centered tetrahedra. The outcomes of this + work are useful for studying phase transitions in other metal-organic + frameworks (MOFs) and may thus guide the development of MOF glasses.}, +} + +@Article{Prasnikar_ArtifIntellRev_2024_v57_p102, + author = {Eva Pra{\v{s}}nikar and Martin Ljubi{\v{c}} and Andrej Perdih and Jure + Bori{\v{s}}ek}, + title = {{Machine learning heralding a new development phase in molecular + dynamics simulations}}, + journal = {Artif Intell Rev}, + year = 2024, + volume = 57, + number = 4, + pages = 102, + doi = {10.1007/s10462-024-10731-4}, + abstract = {AbstractMolecular + dynamics (MD) simulations are a key computational chemistry technique + that provide dynamic insight into the underlying atomic-level + processes in the system under study. These insights not only improve + our understanding of the molecular world, but also aid in the design + of experiments and targeted interventions. Currently, MD is associated + with several limitations, the most important of which are: + insufficient sampling, inadequate accuracy of the atomistic models, + and challenges with proper analysis and interpretation of the obtained + trajectories. Although numerous efforts have been made to address + these limitations, more effective solutions are still needed. The + recent development of artificial intelligence, particularly machine + learning (ML), offers exciting opportunities to address the challenges + of MD. In this review we aim to familiarize readers with the basics of + MD while highlighting its limitations. The main focus is on exploring + the integration of deep learning with MD simulations. The advancements + made by ML are systematically outlined, including the development of + ML-based force fields, techniques for improved conformational space + sampling, and innovative methods for trajectory analysis. + Additionally, the challenges and implications associated with the + integration of ML and artificial intelligence are discussed. While the + potential of ML-MD fusion is clearly established, further applications + are needed to confirm its superiority over traditional methods. This + comprehensive overview of the new perspectives of MD, which ML has + opened up, serves as a gentle introduction to the exciting phase of MD + development.}, +} + +@Article{Zhang_SciChinaMater_2024_v67_p1129, + author = {Cun Zhang and Bolin Yang and Zhilong Peng and Shaohua Chen}, + title = {{Machine learning-based prediction of mechanical properties of N-doped $\gamma$-graphdiyne +}}, + journal = {Sci. China Mater.}, + year = 2024, + volume = 67, + number = 4, + pages = {1129--1139}, + doi = {10.1007/s40843-023-2733-7}, +} + +@Article{Fu_JNuclMater_2024_v591_p154897, + author = {Baoqin Fu and Yandong Sun and Wanrun Jiang and Fu Wang and Linfeng + Zhang and Han Wang and Ben Xu}, + title = {{Determining the thermal conductivity and phonon behavior of SiC + materials with quantum accuracy via deep learning interatomic + potential model}}, + journal = {J. Nucl. Mater.}, + year = 2024, + volume = 591, + pages = 154897, + doi = {10.1016/j.jnucmat.2024.154897}, +} + +@Article{Obeid_JEnergyStorage_2024_v82_p110587, + author = {Mohammed M. Obeid and Jiahui Liu and Penghu Du and Tongyu Liu and + Qiang Sun}, + title = {{A 3D metallic porous sulfurized carbon anode identified by global + structure search for Na-ion batteries with fast diffusion kinetics}}, + journal = {J. Energy Storage}, + year = 2024, + volume = 82, + pages = 110587, + doi = {10.1016/j.est.2024.110587}, +} + +@Article{Xu_JApplPhys_2024_v135, + author = {Fei-Yang Xu and Zhi-Guo Li and Xiang-Rong Chen and Hua Y. Geng and Lei + Liu and Jianbo Hu}, + title = {{Probing the critical point of MgSiO3 using deep potential simulation}}, + journal = {J. Appl. Phys.}, + year = 2024, + volume = 135, + number = 12, + doi = {10.1063/5.0189696}, + abstract = {The giant impact between proto- + Earth and a Mars-sized planet called Theia resulted in the formation + of the Earth{\textendash}Moon system, and the silicate mantles of the + initial bodies may have partly been vaporized. Here, we develop a + machine learning potential for MgSiO3 based on the data from first- + principles calculations to estimate its critical point. The variations + in pressure along different isotherms yield the position of the + critical point of MgSiO3 at 0.54{\,}g{\,}cm{\ensuremath{-}}3 and + 6750{\,}{\ensuremath{\pm}}{\,}250{\,}K, which agrees with the previous + theoretical estimation. We also simulate the MgSiO3 melt under a + spectrum of critical conditions to understand the changes in + coordination environment with density and temperature. The fourfold + Si{\textendash}O coordination hardly changes with increasing density + at 3000{\,}K. However, with increasing temperature, the dominance of + four-coordinated Si{\textendash}O diminishes rapidly as density + decreases. Regarding Mg{\textendash}O coordination, the overall trend, + which varies with temperature and density, remains largely consistent + with Si{\textendash}O but with a greater diversity in the types of + coordination due to more bond breaking events. Our work opens a new + avenue by employing machine learning methods to estimate the critical + point of silicates.}, +} + +@Article{Chang_AcsApplMaterInterfaces_2024_v16_p14954, + author = {Xiaoya Chang and Yongchao Wu and Qingzhao Chu and Gang Zhang and + Dongping Chen}, + title = {{Ab Initio Driven Exploration on the Thermal Properties of Al-Li Alloy}}, + journal = {Acs Appl. Mater. Interfaces}, + year = 2024, + volume = 16, + number = 12, + pages = {14954--14964}, + doi = {10.1021/acsami.4c01480}, + abstract = {Al-Li alloys are feasible and promising additives in advanced energy + and propellant systems due to the significantly enhanced heat release + and increased specific impulse. The thermal properties of Al-Li alloys + directly determine the manufacturing, storage safety, and ignition + delay of propellants. In this study, a neural network potential (NNP) + is developed to investigate the thermal behaviors of Al-Li alloys from + an atomistic perspective. The novel NNP demonstrates an excellent + predictive ability for energy, atomic force, mechanical behaviors, + phonon vibrations, and dynamic evolutions. A series of NNP-based + molecular dynamics simulations are performed to investigate the effect + of Li doping on the thermal properties of Al-Li alloys. All calculated + results for Al-Li alloys are consistent with experimental values for + Al, ensuring their validity in predicting Al-Li interactions. The + simulation results suggest that a minor increment in the Li content + results in a slight change in the melting point, thermal expansion, + and radical distribution functions. These three properties are + associated with the lattice characteristics; nonetheless, it causes a + substantial reduction in thermal conductivity, which is related to the + physical properties of the elements. The lower thermal conductivity + allows heat accumulation on the particle surface, thereby speeding up + the surface premelt and ignition. This provides an alternative atomic + explanation for the improved combustion performance of Al-Li alloys. + These findings integrate insights from the field of alloy material + science into crucial combustion applications, serving as an atomistic + guide for developing manufacturing techniques.}, +} + +@Article{Li_PhysChemChemPhys_2024_v26_p12044, + author = {Xuejiao Li and Tingrui Xu and Yu Gong}, + title = {{Compositional transferability of deep potential in molten LiF-BeF2 and + LaF3 mixtures: prediction of density, viscosity, and local structure}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2024, + volume = 26, + number = 15, + pages = {12044--12052}, + doi = {10.1039/d4cp00079j}, + abstract = {The accumulation of lanthanide fission products carries the risk of + altering the structure and properties of the nuclear fuel carrier salt + LiF-BeF2 (Flibe), thereby downgrading the operating efficiency and + safety of the molten salt reactor. However, the condition-limited + experimental measurements, spatiotemporal-limited first-principles + calculations, and accuracy-limited classical dynamic simulations are + unable to capture the precise local structure and reliable + thermophysical properties of heterogeneous molten salts. Therefore, + the deep potential (DP) of LaF3 and Flibe molten mixtures is developed + here, and DP molecular dynamics simulations are performed to + systemically study the densities, diffusion coefficients, viscosities, + radial distribution functions and coordination numbers of multiple + molten Flibe + xLaF3, the quantitative relationships between these + properties and LaF3 concentration are investigated, and the potential + structure-property relationships are analyzed. Eventually, the + transferability of DP on molten Flibe + LaF3 with different + formulations as well as the predictability of structures and + properties are achieved at the nanometer spatial scale and the + nanosecond timescale.}, +} + +@Article{Liu_ChemMaterPublAmChemSoc_2024_v36_p2898, + author = {Dongyu Liu and Yifan Wu and Mikhail R. Samatov and Andrey S. Vasenko + and Evgueni V. Chulkov and Oleg V. Prezhdo}, + title = {{Compression Eliminates Charge Traps by Stabilizing Perovskite Grain + Boundary Structures: An Ab Initio Analysis with Machine Learning Force + Field}}, + journal = {Chem. Mater.: Publ. Am. Chem. Soc.}, + year = 2024, + volume = 36, + number = 6, + pages = {2898--2906}, + doi = {10.1021/acs.chemmater.3c03261}, + abstract = {Grain boundaries (GBs) play an important role in determining the + optoelectronic properties of perovskites, requiring an atomistic + understanding of the underlying mechanisms. Strain engineering has + recently been employed in perovskite solar cells, providing a novel + perspective on the role of perovskite GBs. Here, we theoretically + investigate the impact of axial strain on the geometric and electronic + properties of a common CsPbBr3 GB. We develop a machine learning force + field and perform ab initio calculations to analyze the behavior of GB + models with different axial strains on a nanosecond time scale. Our + results demonstrate that compressing the GB efficiently suppresses + structural fluctuations and eliminates trap states originating from + large-scale distortions. The GB becomes more amorphous under + compressive strain, which makes the relationship between the + electronic structure and axial strain nonmonotonic. These results can + help clarify the conflicts in perovskite GB experiments.}, +} + +@Article{delaPuente_JPhysChemLett_2024_v15_p3096, + author = {Miguel {\{}de la Puente{\}} and Axel Gomez and Damien Laage}, + title = {{Neural Network-Based Sum-Frequency Generation Spectra of Pure and + Acidified Water Interfaces with Air}}, + journal = {J. Phys. Chem. Lett.}, + year = 2024, + volume = 15, + number = 11, + pages = {3096--3102}, + doi = {10.1021/acs.jpclett.4c00113}, + abstract = {The affinity of hydronium ions (H3O+) for the air-water interface is a + crucial question in environmental chemistry. While sum-frequency + generation (SFG) spectroscopy has been instrumental in indicating the + preference of H3O+ for the interface, key questions persist regarding + the molecular origin of the SFG spectral changes in acidified water. + Here we combine nanosecond long neural network (NN) reactive + simulations of pure and acidified water slabs with NN predictions of + molecular dipoles and polarizabilities to calculate SFG spectra of + long reactive trajectories including proton transfer events. Our + simulations show that H3O+ ions cause two distinct changes in phase- + resolved SFG spectra: first, a low-frequency tail due to the + vibrations of H3O+ and its first hydration shell, analogous to the + bulk proton continuum, and second, an enhanced hydrogen-bonded band + due to the ion-induced static field polarizing molecules in deeper + layers. Our calculations confirm that changes in the SFG spectra of + acidic solutions are caused by hydronium ions preferentially residing + at the interface.}, +} + +@Article{Xiao_PhysChemChemPhys_2024_v26_p11867, + author = {Hang Xiao and Bin Yang}, + title = {{A neural network potential energy surface assisted molecular dynamics + study on the pyrolysis behavior of two spiro-hydrocarbons}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2024, + volume = 26, + number = 15, + pages = {11867--11879}, + doi = {10.1039/d3cp05425j}, + abstract = {Spiro-hydrocarbons are potentially a type of novel alternative jet + fuel due to their high density and net heat of combustion. In this + work, the pyrolysis study of two spiro-hydrocarbons + (spiro[cyclopropane-1,6'-tricyclo[3.2.1.02,4]octane] (C10H14) as Fuel + 1 and spiro[bicyclo[2.2.1]heptane-2,1'-cyclopropane] (C9H14) as Fuel + 2) is performed via molecular dynamics (MD) simulations, with a neural + network potential energy surface (NNPES), deep potential (DP) model, + adopted. The data set for the DP model of each fuel is constructed + after 31 and 27 iterations, respectively. The high precision of the DP + model is demonstrated, and the temperature transferability of each + model is observed. The overall pyrolysis performance is evaluated with + the fuel decomposition rate, showing that both fuels have comparable + gas-reactivity to commercial aviation fuels, such as JP-10. The + reaction networks of initial pyrolysis for Fuels 1 and 2 are + constructed, and the contribution of each pathway is discussed. Fuel 1 + tends to form an unsaturated six-membered ring structure, while Fuel 2 + generates unsaturated open-chain hydrocarbons. Further analyses of the + MD results provide time-evolution information on each component in the + pyrolysis species pool. Compared to Fuel 1, the initial pyrolysis of + Fuel 2 leads to more hydrogen, alkenes, and alkanes, as well as fewer + monocyclic aromatic hydrocarbons (MAHs), demonstrating a reduced + tendency for afterward coking. This work might contribute to the + development of the mechanism of the two spiro-hydrocarbons and guide + the research of other similar structural fuels.}, +} + +@Article{Peng_GeophysResLett_2024_v51, + author = {Yihang Peng and Jie Deng}, + title = {{Thermal Conductivity of MgSiO3{\ens + uremath{<}}/sub>{- + }H2}}O System Determined by Machine Learning Potentials}}, + journal = {Geophys. Res. Lett.}, + year = 2024, + volume = 51, + number = 5, + doi = {10.1029/2023GL107245}, + abstract = {AbstractThermal + conductivity plays a pivotal role in understanding the dynamics and + evolution of Earth's interior. The Earth's lower mantle is dominated + by MgSiO3 polymorphs which may incorporate trace amounts of + water. However, the thermal conductivity of MgSiO3{- + }H2}}O binary system remains poorly understood. Here, we + calculate the thermal conductivity of water{-}free and water{-}bearing + bridgmanite, post{-}perovskite, and MgSiO}}3 melt, using a + combination of Green{-}Kubo method with molecular dynamics simulations + based on a machine learning potential of ab initio quality. The + thermal conductivities of water{-}free bridgmanite and + post{-}perovskite overall agree well with previous theoretical and + experimental studies. The presence of water mildly reduces the thermal + conductivity of the host minerals, significantly weakens the + temperature dependence of the thermal conductivity, and reduces the + thermal anisotropy of post{-}perovskite. Overall, water reduces the + thermal conductivity difference between bridgmanite and + post{-}perovskite, and thus may attenuate lateral heterogeneities of + the core{-}mantle boundary heat + flux.}, +} + +@Article{Pang_PhysChemChemPhys_2024_v26_p11545, + author = {Kehui Pang and Mingjie Wen and Xiaoya Chang and Yabei Xu and Qingzhao + Chu and Dongping Chen}, + title = {{The thermal decomposition mechanism of RDX/AP composites: ab initio + neural network MD simulations}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2024, + volume = 26, + number = 15, + pages = {11545--11557}, + doi = {10.1039/d3cp05709g}, + abstract = {A neural network potential (NNP) is developed to investigate the + decomposition mechanism of RDX, AP, and their composites. Utilizing an + ab initio dataset, the NNP is evaluated in terms of atomic energy and + forces, demonstrating strong agreement with ab initio calculations. + Numerical stability tests across a range of timesteps reveal excellent + stability compared to the state-of-the-art ReaxFF models. Then the + thermal decomposition of pure RDX, AP, and RDX/AP composites is + performed using NNP to explore the coupling effect between RDX and AP. + The results highlight a dual interaction between RDX and AP, i.e., AP + accelerates RDX decomposition, particularly at low temperatures, and + RDX promotes AP decomposition. Analyzing RDX trajectories at the + RDX/AP interface unveils a three-part decomposition mechanism + involving N-N bond cleavage, H transfer with AP to form Cl-containing + acid, and chain-breaking reactions generating small molecules such as + N2, CO, and CO2. The presence of AP enhances H transfer reactions, + contributing to its role in promoting RDX decomposition. This work + studies the reaction kinetics of RDX/AP composites from the atomic + point of view, and can be widely used in the establishment of reaction + kinetics models of composite systems with energetic materials.}, +} + +@Article{Gan_PhysRevB_2024_v109_p115129, + author = {Bo Gan and Jun Li and Junjie Gao and Qiru Zeng and Wenhao Song and + Yukai Zhuang and Yingxin Hua and Qiang Wu and Gang Jiang and Yuan Yin + and Youjun Zhang}, + title = {{Electrical conductivity of copper under ultrahigh pressure and + temperature conditions by both experiments and first-principles + simulations}}, + journal = {Phys. Rev. B}, + year = 2024, + volume = 109, + number = 11, + pages = 115129, + doi = {10.1103/PhysRevB.109.115129}, +} + +@Article{Soshnikov_JChemPhys_2024_v160_p094117, + author = {Artem Soshnikov and Rebecca Lindsey and Ambarish Kulkarni and Nir + Goldman}, + title = {{A reactive molecular dynamics model for uranium/hydrogen containing + systems}}, + journal = {J. Chem. Phys.}, + year = 2024, + volume = 160, + number = 9, + pages = 094117, + doi = {10.1063/5.0183610}, + abstract = {Uranium-based materials are valuable assets in the energy, medical, + and military industries. However, understanding their sensitivity to + hydrogen embrittlement is particularly challenging due to the toxicity + of uranium and the computationally expensive nature of quantum-based + methods generally required to study such processes. In this regard, we + have developed a Chebyshev Interaction Model for Efficient Simulation + (ChIMES) that can be employed to compute energies and forces of U and + UH3 bulk structures with vacancies and hydrogen interstitials with + accuracy similar to that of Density Functional Theory (DFT) while + yielding linear scaling and orders of magnitude improvement in + computational efficiency. We show that the bulk structural parameters, + uranium and hydrogen vacancy formation energies, and diffusion + barriers predicted by the ChIMES potential are in strong agreement + with the reference DFT data. We then use ChIMES to conduct molecular + dynamics simulations of the temperature-dependent diffusion of a + hydrogen interstitial and determine the corresponding diffusion + activation energy. Our model has particular significance in studies of + actinides and other high-Z materials, where there is a strong need for + computationally efficient methods to bridge length and time scales + between experiments and quantum theory.}, +} + +@Article{Liu_JPhysChemA_2024_v128_p1656, + author = {Ziyi Liu and An-Hui Lu and Dongqi Wang}, + title = {{Deep Potential Molecular Dynamics Study of Propane Oxidative + Dehydrogenation}}, + journal = {J. Phys. Chem., A}, + year = 2024, + volume = 128, + number = 9, + pages = {1656--1664}, + doi = {10.1021/acs.jpca.3c07859}, + abstract = {Oxidative dehydrogenation (ODH) of light alkanes is a key process in + the oxidative conversion of alkanes to alkenes, oxygenated + hydrocarbons, and COx (x = 1,2). Understanding the underlying + mechanisms extensively is crucial to keep the ODH under control for + target products, e.g., alkenes rather than COx, with minimal energy + consumption, e.g., during the alkene production or maximal energy + release, e.g., during combustion. In this work, deep potential (DP), a + neural network atomic potential developed in recent years, was + employed to conduct large-scale accurate reactive dynamic simulations. + The model was trained on a sufficient data set obtained at the density + functional theory level. The intricate reaction network was elucidated + and organized in the form of a hierarchical network to demonstrate the + key features of the ODH mechanisms, including the activation of + propane and oxygen, the influence of propyl reaction pathways on the + propene selectivity, and the role of rapid H2O2 decomposition for + sustainable and efficient ODH reactions. The results indicate the more + complex reaction mechanism of propane ODH than that of ethane ODH and + are expected to provide insights in the ODH catalyst optimization. In + addition, this work represents the first application of deep potential + in the ODH mechanistic study and demonstrates the ample advantages of + DP in the study of mechanism and dynamics of complex systems.}, +} + +@Article{Ojih_JMaterChemA_2024_v12_p8502, + author = {Joshua Ojih and Mohammed Al-Fahdi and Yagang Yao and Jianjun Hu and + Ming Hu}, + title = {{Graph theory and graph neural network assisted high-throughput crystal + structure prediction and screening for energy conversion and storage}}, + journal = {J. Mater. Chem. A}, + year = 2024, + volume = 12, + number = 14, + pages = {8502--8515}, + doi = {10.1039/d3ta06190f}, + abstract = {Prediction of crystal structures + with desirable material properties is a grand challenge in materials + research. We deployed graph theory assisted structure searcher and + combined with universal machine learning potentials to accelerate the + process.}, +} + +@Article{Ren_EnergyEnvSci_2024_v17_p2743, + author = {Fucheng Ren and Yuqi Wu and Wenhua Zuo and Wengao Zhao and Siyuan Pan + and Hongxin Lin and Haichuan Yu and Jing Lin and Min Lin and Xiayin + Yao and Torsten Brezesinski and Zhengliang Gong and Yong Yang}, + title = {{Visualizing the SEI formation between lithium metal and solid-state + electrolyte}}, + journal = {Energy Env., Sci,}, + year = 2024, + volume = 17, + number = 8, + pages = {2743--2752}, + doi = {10.1039/d3ee03536k}, + abstract = {Large-scale molecular dynamics + simulations reveal the formation mechanism and structure of the solid + electrolyte interphase between lithium metal and {\ensuremath{\beta}}- + Li3}}PS4{\ensuremath{ + <}}/jats:sub> in all-solid-state + batteries.}, +} + +@Article{Liu_ChemSci_2024_v15_p5294, + author = {Shanping Liu and Romain Dupuis and Dong Fan and Salma Benzaria and + Mickaele Bonneau and Prashant Bhatt and Mohamed Eddaoudi and Guillaume + Maurin}, + title = {{Machine learning potential for modelling H2 adsorption/diffusion in + MOFs with open metal sites}}, + journal = {Chem. Sci.}, + year = 2024, + volume = 15, + number = 14, + pages = {5294--5302}, + doi = {10.1039/d3sc05612k}, + abstract = {Metal-organic frameworks (MOFs) incorporating open metal sites (OMS) + have been identified as promising sorbents for many societally + relevant-adsorption applications including CO2 capture, natural gas + purification and H2 storage. This has been ascribed to strong specific + interactions between OMS and the guest molecules that enable the MOF + to achieve an effective capture even under low gas pressure + conditions. In particular, the presence of OMS in MOFs was + demonstrated to substantially boost the H2 binding energy for + achieving high adsorbed hydrogen densities and large usable hydrogen + capacities. So far, there is a critical bottleneck to computationally + attain a full understanding of the thermodynamics and dynamics of H2 + in this sub-class of MOFs since the generic classical force fields + (FFs) are known to fail to accurately describe the interactions + between OMS and any guest molecules, in particular H2. This clearly + hampers the computational-assisted identification of MOFs containing + OMS for a target adsorption-related application since the standard + high-throughput screening approach based on these generic FFs is not + applicable. Therefore, there is a need to derive novel FFs to achieve + accurate and effective evaluation of MOFs for H2 adsorption. On this + path, as a proof-of-concept, the soc-MOF-1d containing OMS, previously + envisaged as a potential platform for H2 adsorption, was selected as a + benchmark material and a machine learning potential (MLP) was derived + for the Al-soc-MOF-1d from a dataset initially generated by ab initio + molecular dynamics (AIMD) simulations. This MLP was further + implemented in MD simulations to explore the H2 binding modes as well + as the temperature dependence distribution of H2 in the MOF pores from + 10 K to 80 K. MLP-Grand Canonical Monte Carlo (GCMC) simulations were + then performed to predict the H2 sorption isotherm of Al-soc-MOF-1d at + 77 K that was further confirmed using sorption data we collected on + this sample. As a further step, MLP-based molecular dynamics (MD) + simulations were conducted to anticipate the kinetics of H2 in this + MOF. This work delivers the first MLP able to describe accurately the + interactions between the challenging H2 guest molecule and MOFs + containing OMS. This innovative strategy applied to one of the most + complex molecules owing to its highly polarizable nature, paves the + way towards a more systematic accurate and efficient in silico + assessment of MOFs containing OMS for H2 adsorption and beyond to the + low-pressure capture of diverse molecules.}, +} + +@Article{Urata_PhysRevMater_2024_v8_p33602, + author = {Shingo Urata and Aik Rui Tan and Rafael G{\'o}mez-Bombarelli}, + title = {{Modifying ring structures in lithium borate glasses under compression: + MD simulations using a machine-learning potential}}, + journal = {Phys, Rev, Mater.}, + year = 2024, + volume = 8, + number = 3, + pages = 33602, + doi = {10.1103/PhysRevMaterials.8.033602}, +} + +@Article{Wan_PhysRevB_2024_v109_p94101, + author = {Tianqi Wan and Chenxing Luo and Yang Sun and Renata M. Wentzcovitch}, + title = {{Thermoelastic properties of bridgmanite using deep-potential molecular + dynamics}}, + journal = {Phys. Rev. B}, + year = 2024, + volume = 109, + number = 9, + pages = 94101, + doi = {10.1103/PhysRevB.109.094101}, + abstract = {MgSiO{\_}3-perovskite (MgPv) plays a crucial role in the Earth's lower + mantle. This study combines deep-learning potential (DP) with density + functional theory (DFT) to investigate the structural and elastic + properties of MgPv under lower mantle conditions. To simulate complex + systems, we developed a series of potentials capable of faithfully + reproducing DFT calculations using different functionals, such as LDA, + PBE, PBEsol, and SCAN meta-GGA functionals. The obtained predictions + exhibit remarkable reliability and consistency, closely resembling + experimental measurements. Our results highlight the superior + performance of the DP-SCAN and DP-LDA in accurately predicting high- + temperature equations of states and elastic properties. This hybrid + computational approach offers a solution to the accuracy-efficiency + dilemma in obtaining precise elastic properties at high pressure and + temperature conditions for minerals like MgPv, which opens a new way + to study the Earth's interior state and related processes.}, +} + +@Article{Ryu_JMolLiq_2024_v397_p124054, + author = {Jae Hyun Ryu and Ji Woong Yu and Tae Jun Yoon and Won Bo Lee}, + title = {{Understanding the dielectric relaxation of liquid water using neural + network potential and classical pairwise potential}}, + journal = {J. Mol. Liq.}, + year = 2024, + volume = 397, + pages = 124054, + doi = {10.1016/j.molliq.2024.124054}, +} + +@Article{Gardner_MachLearnSciTechnol_2024_v5_p15003, + author = {John L A Gardner and Kathryn T Baker and Volker L Deringer}, + title = {{Synthetic pre-training for neural-network interatomic potentials}}, + journal = {Mach, Learn.,: Sci, Technol,}, + year = 2024, + volume = 5, + number = 1, + pages = 15003, + doi = {10.1088/2632-2153/ad1626}, + abstract = {Abstract + Machine learning (ML) based + interatomic potentials have transformed the field of atomistic + materials modelling. However, ML potentials depend critically on the + quality and quantity of quantum-mechanical reference data with which + they are trained, and therefore developing datasets and training + pipelines is becoming an increasingly central challenge. Leveraging + the idea of {\textquoteleft}synthetic{\textquoteright} (artificial) + data that is common in other areas of ML research, we here show that + synthetic atomistic data, themselves obtained at scale with an + existing ML potential, constitute a useful pre-training task for + neural-network (NN) interatomic potential models. Once pre-trained + with a large synthetic dataset, these models can be fine-tuned on a + much smaller, quantum-mechanical one, improving numerical accuracy and + stability in computational practice. We demonstrate feasibility for a + series of equivariant graph-NN potentials for carbon, and we carry out + initial experiments to test the limits of the + approach.}, +} + +@Article{Liu_GeosciFront_2024_v15_p101735, + author = {Jie Liu and Tao Zhang and Shuyu Sun}, + title = {{Review of deep learning algorithms in molecular simulations and + perspective applications on petroleum engineering}}, + journal = {Geosci. Front.}, + year = 2024, + volume = 15, + number = 2, + pages = 101735, + doi = {10.1016/j.gsf.2023.101735}, +} + +@Article{Yang_Arxiv_2024_v135, + author = {Guang Yang and Yuan-Bin Liu and Lei Yang and Bing-Yang Cao}, + title = {{Machine-learned atomic cluster expansion potentials for fast and + quantum-accurate thermal simulations of wurtzite AlN}}, + journal = {Arxiv}, + year = 2024, + volume = 135, + number = 8, + doi = {10.1063/5.0188905}, + abstract = {Thermal transport in wurtzite + aluminum nitride (w-AlN) significantly affects the performance and + reliability of corresponding electronic devices, particularly when + lattice strains inevitably impact the thermal properties of w-AlN in + practical applications. To accurately model the thermal properties of + w-AlN with high efficiency, we develop a machine learning interatomic + potential based on the atomic cluster expansion (ACE) framework. The + predictive power of the ACE potential against density functional + theory (DFT) is demonstrated across a broad range of properties of + w-AlN, including ground-state lattice parameters, specific heat + capacity, coefficients of thermal expansion, bulk modulus, and + harmonic phonon dispersions. Validation of lattice thermal + conductivity is further carried out by comparing the ACE-predicted + values to the DFT calculations and experiments, exhibiting the overall + capability of our ACE potential in sufficiently describing anharmonic + phonon interactions. As a practical application, we perform a lattice + dynamics analysis using the potential to unravel the effects of + biaxial strains on thermal conductivity and phonon properties of + w-AlN, which is identified as a significant tuning factor for near- + junction thermal design of w-AlN-based + electronics.}, +} + +@Article{Zhang_ComputMaterSci_2024_v235_p112843, + author = {Shihao Zhang and Fanshun Meng and Rong Fu and Shigenobu Ogata}, + title = {{Highly efficient and transferable interatomic potentials for + $\alpha$-iron and $\alpha$-iron/hydrogen binary systems using deep neural networks}}, + journal = {Comput. Mater. Sci.}, + year = 2024, + volume = 235, + pages = 112843, + doi = {10.1016/j.commatsci.2024.112843}, +} + +@Article{Bertani_JChemTheoryComput_2024_v20_p1358, + author = {Marco Bertani and Thibault Charpentier and Francesco Faglioni and + Alfonso Pedone}, + title = {{Accurate and Transferable Machine Learning Potential for Molecular + Dynamics Simulation of Sodium Silicate Glasses}}, + journal = {J. Chem. Theory Comput.}, + year = 2024, + volume = 20, + number = 3, + pages = {1358--1370}, + doi = {10.1021/acs.jctc.3c01115}, + abstract = {An accurate and transferable machine learning (ML) potential for the + simulation of binary sodium silicate glasses over a wide range of + compositions (from 0 to 50{\%} Na2O) was developed. The potential + energy surface is approximated by the sum of atomic energy + contributions mapped by a neural network algorithm from the local + geometry comprising information on atomic distances and angles with + neighboring atoms using the DeePMD code [Wang, H. Comput. Phys. + Commun. 2018, 228, 178-184]. Our model was trained on a large data set + of total energies and atomic forces computed at the density functional + theory level on structures extracted from classical molecular dynamics + (MD) simulations performed at several temperatures from 300 to 3000 K. + This allows for the generation of a robust and transferable ML + potential applicable over the full compositional range of glass + formability at different temperatures that outperforms the empirical + potentials available in the literature in reproducing structures and + properties such as bond angle distribution, total distribution + functions, and vibrational density of state. The generality of the + approach enables the future training of a potential with other or more + elements allowing for simulations of structures, properties, and + behavior of ternary and multicomponent oxide glasses with nearly ab + initio accuracy at a fraction of the computational cost.}, +} + +@Article{BinFaheem_JPhysChemC_2024_v128_p2163, + author = {Abdullah {\{}Bin Faheem{\}} and Kyung-Koo Lee}, + title = {{Development of a Neural Network Potential for Modeling Molten LiCl/KCl + Salts: Bridging Efficiency and Accuracy}}, + journal = {J. Phys. Chem. C}, + year = 2024, + volume = 128, + number = 5, + pages = {2163--2178}, + doi = {10.1021/acs.jpcc.3c07010}, +} + +@Article{Guo_AcsCatal_2024_v14_p1232, + author = {Jiawei Guo and Tyler Sours and Sam Holton and Chenghan Sun and + Ambarish R. Kulkarni}, + title = {{Screening Cu-Zeolites for Methane Activation Using Curriculum-Based + Training}}, + journal = {Acs Catal.}, + year = 2024, + volume = 14, + number = 3, + pages = {1232--1242}, + doi = {10.1021/acscatal.3c05275}, + abstract = {Machine learning (ML), when used synergistically with atomistic + simulations, has recently emerged as a powerful tool for accelerated + catalyst discovery. However, the application of these techniques has + been limited by the lack of interpretable and transferable ML models. + In this work, we propose a curriculum-based training (CBT) philosophy + to systematically develop reactive machine learning potentials (rMLPs) + for high-throughput screening of zeolite catalysts. Our CBT approach + combines several different types of calculations to gradually teach + the ML model about the relevant regions of the reactive potential + energy surface. The resulting rMLPs are accurate, transferable, and + interpretable. We further demonstrate the effectiveness of this + approach by exhaustively screening thousands of [CuOCu]2+ sites across + hundreds of Cu-zeolites for the industrially relevant methane + activation reaction. Specifically, this large-scale analysis of the + entire International Zeolite Association (IZA) database identifies a + set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) + that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed + methane activation. We believe that this CBT philosophy can be + generally applied to other zeolite-catalyzed reactions and, + subsequently, to other types of heterogeneous catalysts. Thus, this + represents an important step toward overcoming the long-standing + barriers within the computational heterogeneous catalysis community.}, +} + +@Article{Gong_PhysRevB_2024_v109_p54117, + author = {Zhanpeng Gong and Jefferson Zhe Liu and Xiangdong Ding and Jun Sun and + Junkai Deng}, + title = {{Strain-induced two-dimensional relaxor ferroelectrics in Se-doped PbTe}}, + journal = {Phys. Rev. B}, + year = 2024, + volume = 109, + number = 5, + pages = 54117, + doi = {10.1103/PhysRevB.109.054117}, +} + +@Article{Zhang_PhysRevAppl_2024_v21_p24043, + author = {Pan Zhang and Dan Jin and Mi Qin and Zhenhua Zhang and Yong Liu and + Ziyu Wang and Zhihong Lu and Rui Xiong and Jing Shi}, + title = {{Effects of four-phonon interaction and vacancy defects on the thermal + conductivity of the low-temperature phase of SnSe}}, + journal = {Phys, Rev, Appl.}, + year = 2024, + volume = 21, + number = 2, + pages = 24043, + doi = {10.1103/PhysRevApplied.21.024043}, +} + +@Article{Ding_IntJMolSci_2024_v25_p1448, + author = {Ye Ding and Jing Huang}, + title = {{Implementation and Validation of an OpenMM Plugin for the Deep + Potential Representation of Potential Energy}}, + journal = {Int. J. Mol. Sci.}, + year = 2024, + volume = 25, + number = 3, + pages = 1448, + doi = {10.3390/ijms25031448}, + abstract = {Machine learning potentials, particularly the deep potential (DP) + model, have revolutionized molecular dynamics (MD) simulations, + striking a balance between accuracy and computational efficiency. To + facilitate the DP model's integration with the popular MD engine + OpenMM, we have developed a versatile OpenMM plugin. This plugin + supports a range of applications, from conventional MD simulations to + alchemical free energy calculations and hybrid DP/MM simulations. Our + extensive validation tests encompassed energy conservation in + microcanonical ensemble simulations, fidelity in canonical ensemble + generation, and the evaluation of the structural, transport, and + thermodynamic properties of bulk water. The introduction of this + plugin is expected to significantly expand the application scope of DP + models within the MD simulation community, representing a major + advancement in the field.}, +} + +@Article{Uporov_Intermetallics_2024_v165_p108121, + author = {S.A. Uporov and E.V. Sterkhov and I.A. Balyakin and V.A. Bykov and + I.S. Sipatov and A.A. Rempel}, + title = {{Synthesis and magnetic properties of some monotectic composites + containing ultra-dispersed particles of YGdTbDyHo high-entropy alloy}}, + journal = {Intermetallics}, + year = 2024, + volume = 165, + pages = 108121, + doi = {10.1016/j.intermet.2023.108121}, +} + +@Article{Kamath_JPhysChemA_2024_v128_p807, + author = {Ganesh Kamath and Alexey Illarionov and Serzhan Sakipov and Leonid + Pereyaslavets and Igor V. Kurnikov and Oleg Butin and Ekaterina + Voronina and Ilya Ivahnenko and Igor Leontyev and Grzegorz Nawrocki + and Mikhail Darkhovskiy and Michael Olevanov and Yevhen K. + Cherniavskyi and Christopher Lock and Sean Greenslade and YuChun Chen + and Roger D. Kornberg and Michael Levitt and Boris Fain}, + title = {{Combining Force Fields and Neural Networks for an Accurate + Representation of Bonded Interactions}}, + journal = {J. Phys. Chem., A}, + year = 2024, + volume = 128, + number = 4, + pages = {807--812}, + doi = {10.1021/acs.jpca.3c07598}, + abstract = {We present a formalism of a neural network encoding bonded + interactions in molecules. This intramolecular encoding is consistent + with the models of intermolecular interactions previously designed by + this group. Variants of the encoding fed into a corresponding neural + network may be used to economically improve the representation of + torsional degrees of freedom in any force field. We test the accuracy + of the reproduction of the ab initio potential energy surface on a set + of conformations of two dipeptides, methyl-capped ALA and ASP, in + several scenarios. The encoding, either alone or in conjunction with + an analytical potential, improves agreement with ab initio energies + that are on par with those of other neural network-based potentials. + Using the encoding and neural nets in tandem with an analytical model + places the agreements firmly within {''}chemical accuracy{''} of + {\ensuremath{\pm}}0.5 kcal/mol.}, +} + +@Article{Zhai_ApplPhysA_2024_v130_p106, + author = {B. Zhai and J. Chang and G. X. Li and H. P. Wang}, + title = {{Grain refinement mechanism of boron addition within Ti{\textendash}Al + alloy}}, + journal = {Appl. Phys. A}, + year = 2024, + volume = 130, + number = 2, + pages = 106, + doi = {10.1007/s00339-024-07277-1}, +} + +@Article{Guo_PhysChemChemPhys_2024_v26_p6590, + author = {Yuliang Guo and Xiaobo Sun and Handong Jiao and Liwen Zhang and + Wenxuan Qin and Xiaoli Xi and Zuoren Nie}, + title = {{Effect of electric fields on tungsten distribution in Na2WO4-WO3 + molten salt}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2024, + volume = 26, + number = 8, + pages = {6590--6599}, + doi = {10.1039/d3cp06202c}, + abstract = {Tungsten coatings have unique properties such as high melting points + and hardness and are widely used in the nuclear fusion and aviation + fields. In experiments, compared to pure Na2WO4 molten salt, + electrolysis with Na2WO4-WO3 molten salt results in a lower deposition + voltage. Herein, an investigation combining experimental and + computational approaches was conducted, involving molecular dynamics + simulations with deep learning, high-temperature in situ Raman + spectroscopy and activation strain model analysis. The results + indicated that the molten salt system's behaviour, influenced by + migration and polarization effects, led to increased formation of + Na2W2O7 in the Na2WO4-WO3 molten salt, which has a lower decomposition + voltage and subsequently accelerated the cathodic deposition of + tungsten. We analyzed the mechanism of the effect of the electric + field on the Na2W2O7 structure based on the bond strength and electron + density. This research provides crucial theoretical support for the + effect of electric field on tungsten in molten salt and demonstrates + the feasibility of using machine learning-based DPMD methods in + simulating tungsten-containing molten salt systems.}, +} + +@Article{Yang_ChemSci_2024_v15_p3382, + author = {Manyi Yang and Enrico Trizio and Michele Parrinello}, + title = {{Structure and polymerization of liquid sulfur across the + {\ensuremath{\lambda}}-transition}}, + journal = {Chem. Sci.}, + year = 2024, + volume = 15, + number = 9, + pages = {3382--3392}, + doi = {10.1039/d3sc06282a}, + abstract = {The anomalous {\ensuremath{\lambda}}-transition of liquid sulfur, + which is supposed to be related to the transformation of eight- + membered sulfur rings into long polymeric chains, has attracted + considerable attention. However, a detailed description of the + underlying dynamical polymerization process is still missing. Here, we + study the structures and the mechanism of the polymerization processes + of liquid sulfur across the {\ensuremath{\lambda}}-transition as well + as its reverse process of formation of the rings. We do so by + performing ab initio-quality molecular dynamics simulations thanks to + a combination of machine learning potentials and state-of-the-art + enhanced sampling techniques. With our approach, we obtain structural + results that are in good agreement with the experiments and we report + precious dynamical insights into the mechanisms involved in the + process.}, +} + +@Article{Li_ComputMaterSci_2024_v232_p112656, + author = {Tao Li and Qing Hou and Jie-chao Cui and Jia-hui Yang and Ben Xu and + Min Li and Jun Wang and Bao-qin Fu}, + title = {{Deep learning interatomic potential for thermal and defect behaviour + of aluminum nitride with quantum accuracy}}, + journal = {Comput. Mater. Sci.}, + year = 2024, + volume = 232, + pages = 112656, + doi = {10.1016/j.commatsci.2023.112656}, +} + +@Article{Ju_ComputMaterSci_2024_v232_p112664, + author = {Shin-Pon Ju and Chao-Chuan Huang and Hsing-Yin Chen}, + title = {{Illuminating the mechanical responses of amorphous boron nitride + through deep learning: A molecular dynamics study}}, + journal = {Comput. Mater. Sci.}, + year = 2024, + volume = 232, + pages = 112664, + doi = {10.1016/j.commatsci.2023.112664}, +} + +@Article{Wang_PhysChemChemPhys_2024_v26_p6351, + author = {Xin-Xuan Wang and Ting Song and Zhen-Shuai Lei and Xiao-Wei Sun and + Jun-Hong Tian and Zi-Jiang Liu}, + title = {{Study of high-pressure thermophysical properties of orthocarbonate + Sr3CO5 using deep learning molecular dynamics simulations}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2024, + volume = 26, + number = 7, + pages = {6351--6361}, + doi = {10.1039/d3cp04833k}, + abstract = {The exploration of the physical attributes of the recently discovered + orthocarbonate Sr3CO5 is significant for comprehending the carbon + cycle and storage mechanisms within the Earth's interior. In this + study, first-principles calculations are initially used to examine the + structural phase transitions of Sr3CO5 polymorphs within the range of + lower mantle pressures. The results suggest that Sr3CO5 with the Cmcm + phase exhibits a minimal enthalpy between 8.3 and 30.3 GPa. As the + pressure exceeds 30.3 GPa, the Cmcm phase undergoes a transition to + the I4/mcm phase, while the experimentally observed Pnma phase remains + metastable under our studied pressure. Furthermore, the structural + data of SrO, SrCO3, and Sr3CO5 polymorphs are utilized to develop a + deep learning potential model suitable for the Sr-C-O system, and the + pressure-volume relationship and elastic constants calculated using + the potential model are in line with the available results. + Subsequently, the elastic properties of Cmcm and I4/mcm phases in + Sr3CO5 at high temperature and pressure are calculated using the + molecular dynamics method. The results indicate that the I4/mcm phase + exhibits higher temperature sensitivity in terms of elastic moduli and + wave velocities compared to the Cmcm phase. Finally, the thermodynamic + properties of the Cmcm and I4/mcm phases are predicted in the range of + 0-2000 K and 10-120 GPa, revealing that the heat capacity and bulk + thermal expansion coefficient of both phases increase with + temperature, with the constant volume heat capacity gradually + approaching the Dulong-Petit limit as the temperature rises.}, +} + +@Article{Zhang_CellRepPhysSci_2024_v5_p101760, + author = {Junjie Zhang and Hao Zhang and Jing Wu and Xin Qian and Bai Song and + Cheng-Te Lin and Te-Huan Liu and Ronggui Yang}, + title = {{Vacancy-induced phonon localization in boron arsenide using a unified + neural network interatomic potential}}, + journal = {Cell Rep. Phys. Sci.}, + year = 2024, + volume = 5, + number = 1, + pages = 101760, + doi = {10.1016/j.xcrp.2023.101760}, +} + +@Article{Fan_Nanoscale_2024_v16_p3438, + author = {Dong Fan and Aydin Ozcan and Pengbo Lyu and Guillaume Maurin}, + title = {{Unravelling abnormal in-plane stretchability of two-dimensional metal- + organic frameworks by machine learning potential molecular dynamics}}, + journal = {Nanoscale}, + year = 2024, + volume = 16, + number = 7, + pages = {3438--3447}, + doi = {10.1039/d3nr05966a}, + abstract = {Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense + potential for various applications due to their distinctive intrinsic + properties compared to their 3D analogues. Herein, we designed a + highly stable NiF2(pyrazine)2 2D MOF in silico with a two-dimensional + periodic wine-rack architecture. Extensive first-principles + calculations and molecular dynamics (MD) simulations based on a newly + developed machine learning potential (MLP) revealed that this 2D MOF + exhibits huge in-plane Poisson's ratio anisotropy. This results in + anomalous negative in-plane stretchability, as evidenced by an + uncommon decrease in its in-plane area upon the application of + uniaxial tensile strain, which makes this 2D MOF particularly + attractive for flexible wearable electronics and ultra-thin sensor + applications. We further demonstrated the unique capability of MLP to + accurately predict the finite-temperature properties of MOFs on a + large scale, exemplified by MLP-MD simulations with a dimension of + 28.2 {\texttimes} 28.2 nm2, relevant to the length scale + experimentally attainable for the fabrication of MOF films.}, +} + +@Article{Feng_JMolLiq_2024_v394_p123533, + author = {Taixi Feng and Guimin Lu}, + title = {{Hydration MgCl2-NaCl-KCl molten salt using a novel approach for + training machine learning potential}}, + journal = {J. Mol. Liq.}, + year = 2024, + volume = 394, + pages = 123533, + doi = {10.1016/j.molliq.2023.123533}, +} + +@Article{Thakur_JChemPhys_2024_v160_p024502, + author = {Atul C. Thakur and Richard C. Remsing}, + title = {{Nuclear quantum effects in the acetylene:ammonia plastic co-crystal}}, + journal = {J. Chem. Phys.}, + year = 2024, + volume = 160, + number = 2, + pages = 024502, + doi = {10.1063/5.0179161}, + abstract = {Organic molecular solids can exhibit rich phase diagrams. In addition + to structurally unique phases, translational and rotational degrees of + freedom can melt at different state points, giving rise to partially + disordered solid phases. The structural and dynamic disorder in these + materials can have a significant impact on the physical properties of + the organic solid, necessitating a thorough understanding of disorder + at the atomic scale. When these disordered phases form at low + temperatures, especially in crystals with light nuclei, the prediction + of material properties can be complicated by the importance of nuclear + quantum effects. As an example, we investigate nuclear quantum effects + on the structure and dynamics of the orientationally disordered, + translationally ordered plastic phase of the acetylene:ammonia (1:1) + co-crystal that is expected to exist on the surface of Saturn's moon + Titan. Titan's low surface temperature ({\ensuremath{\sim}}90{~}K) + suggests that the quantum mechanical behavior of nuclei may be + important in this and other molecular solids in these environments. By + using neural network potentials combined with ring polymer molecular + dynamics simulations, we show that nuclear quantum effects increase + orientational disorder and rotational dynamics within the + acetylene:ammonia (1:1) co-crystal by weakening hydrogen bonds. Our + results suggest that nuclear quantum effects are important to + accurately model molecular solids and their physical properties in + low-temperature environments.}, +} + +@Article{Kussainova_JChemEngData_2024_v69_p204, + author = {Dina Kussainova and Athanassios Z. Panagiotopoulos}, + title = {{Molecular Simulation of Lithium Carbonate Reactive + Vapor{\textendash}Liquid Equilibria Using a Deep Potential Model}}, + journal = {J. Chem. Eng. Data}, + year = 2024, + volume = 69, + number = 1, + pages = {204--214}, + doi = {10.1021/acs.jced.3c00580}, +} + +@Article{Dong_AcsApplMaterInterfaces_2024_v16_p530, + author = {Wenhao Dong and Heqing Tian and Wenguang Zhang and Jun-Jie Zhou and + Xinchang Pang}, + title = {{Development of NaCl-MgCl2-CaCl2 Ternary Salt for High-Temperature + Thermal Energy Storage Using Machine Learning}}, + journal = {Acs Appl. Mater. Interfaces}, + year = 2024, + volume = 16, + number = 1, + pages = {530--539}, + doi = {10.1021/acsami.3c13412}, + abstract = {NaCl-MgCl2-CaCl2 eutectic ternary chloride salts are potential heat + transfer and storage materials for high-temperature thermal energy + storage. In this study, first-principles molecular dynamics simulation + results were used as a data set to develop an interatomic potential + for ternary chloride salts using a neural network machine learning + method. Deep potential molecular dynamics (DPMD) simulations were + performed to predict the microstructure and thermophysical properties + of the NaCl-MgCl2-CaCl2 ternary salt. This work reveals that DPMD + simulations can accurately calculate the microstructure and + thermophysical properties of ternary chloride salts. The association + strength of chloride ions and cations follows the order of Mg2+ + > Ca2+ > Na+, and the coordination + number decreases gradually with increasing temperature, indicating a + progressively looser and more disordered molten structure. + Furthermore, thermophysical properties, such as density, specific heat + capacity, thermal conductivity, and viscosity, are in good agreement + with the experimental measurements. Machine learning molecular + dynamics will provide a feasible multivariate molten salt exploration + method for the design of next-generation solar power plants and + thermal energy storage systems.}, +} + +@Article{Hedelius_JChemTheoryComput_2024_v20_p199, + author = {Bryce E. Hedelius and Damon Tingey and Dennis {\{}Della Corte{\}}}, + title = {{TrIP-Transformer Interatomic Potential Predicts Realistic Energy + Surface Using Physical Bias}}, + journal = {J. Chem. Theory Comput.}, + year = 2024, + volume = 20, + number = 1, + pages = {199--211}, + doi = {10.1021/acs.jctc.3c00936}, + abstract = {Accurate interatomic energies and forces enable high-quality molecular + dynamics simulations, torsion scans, potential energy surface + mappings, and geometry optimizations. Machine learning algorithms have + enabled rapid estimates of the energies and forces with high accuracy. + Further development of machine learning algorithms holds promise for + producing universal potentials that support many different atomic + species. We present the Transformer Interatomic Potential (TrIP): a + chemically sound potential based on the SE(3)-Transformer. TrIP's + species-agnostic architecture, which uses continuous atomic + representation and homogeneous graph convolutions, encourages + parameter sharing between atomic species for more general + representations of chemical environments, maintains a reasonable + number of parameters, serves as a form of regularization, and is a + step toward accurate universal interatomic potentials. TrIP achieves + state-of-the-art accuracies on the COMP6 benchmark with an energy + prediction of just 1.02 kcal/mol MAE. We introduce physical bias in + the form of Ziegler-Biersack-Littmark-screened nuclear repulsion and + constrained atomization energies. An energy scan of a water molecule + demonstrates that these changes improve long- and near-range + interactions compared to other neural network potentials. TrIP also + demonstrates stability in molecular dynamics simulations, + demonstrating reasonable exploration of Ramachandran space.}, +} + +@Article{Kwon_AcsApplMaterInterfaces_2024_v16_p31687, + author = {Hyuna Kwon and Marcos F. {\{}Calegari Andrade{\}} and Shane Ardo and + Daniel V. Esposito and Tuan Anh Pham and Tadashi Ogitsu}, + title = {{Confinement Effects on Proton Transfer in TiO2 Nanopores from Machine + Learning Potential Molecular Dynamics Simulations}}, + journal = {Acs Appl. Mater. Interfaces}, + year = 2024, + volume = 16, + number = 24, + pages = {31687--31695}, + doi = {10.1021/acsami.4c02339}, + abstract = {Improved understanding of proton transfer in nanopores is critical for + a wide range of emerging applications, yet experimentally probing + mechanisms and energetics of this process remains a significant + challenge. To help reveal details of this process, we developed and + applied a machine learning potential derived from first-principles + calculations to examine water reactivity and proton transfer in TiO2 + slit-pores. We find that confinement of water within pores smaller + than 0.5 nm imposes strong and complex effects on water reactivity and + proton transfer. Although the proton transfer mechanism is similar to + that at a TiO2 interface with bulk water, confinement reduces the + activation energy of this process, leading to more frequent proton + transfer events. This enhanced proton transfer stems from the + contraction of oxygen-oxygen distances dictated by the interplay + between confinement and hydrophilic interactions. Our simulations also + highlight the importance of the surface topology, where faster proton + transport is found in the direction where a unique arrangement of + surface oxygens enables the formation of an ordered water chain. In a + broader context, our study demonstrates that proton transfer in + hydrophilic nanopores can be enhanced by controlling pore size, + surface chemistry, and topology.}, +} + +@Article{Raman_MolPhys_2024, + author = {Abhinav S. Raman and Annabella Selloni}, + title = {{An ab- + initio deep neural + network potential for accurate large-scale simulations of the + muscovite mica-water interface}}, + journal = {Mol. Phys.}, + year = 2024, + doi = {10.1080/00268976.2024.2365430}, +} + +@Article{Urata_JAmCeramSoc_2024, + author = {Shingo Urata and Marco Bertani and Alfonso Pedone}, + title = {{Applications of machine{-}learning interatomic potentials for modeling + ceramics, glass, and electrolytes: A review}}, + journal = {J Am Ceram Soc.}, + year = 2024, + doi = {10.1111/jace.19934}, + abstract = {AbstractThe + emergence of artificial intelligence has provided efficient + methodologies to pursue innovative findings in material science. Over + the past two decades, machine{-}learning potential (MLP) has emerged + as an alternative technology to density functional theory (DFT) and + classical molecular dynamics (CMD) simulations for computational + modeling of materials and estimation of their properties. The MLP + offers more efficient computation compared to DFT, while providing + higher accuracy compared to CMD. This enables us to conduct more + realistic simulations using models with more atoms and for longer + simulation times. Indeed, the number of research studies utilizing + MLPs has significantly increased since 2015, covering a broad range of + materials and their structures, ranging from simple to complex, as + well as various chemical and physical phenomena. As a result, there + are high expectations for further applications of MLPs in the field of + material science and industrial development. This review aims to + summarize the applications, particularly in ceramics and glass + science, and fundamental theories of MLPs to facilitate future + progress and utilization. Finally, we provide a summary and discuss + perspectives on the next challenges in the development and application + of{~}MLPs.}, +} + +@Article{Chen_JChemTheoryComput_2024_v20_p4703, + author = {Yuzhuo Chen and Sebastian V. Pios and Maxim F. Gelin and Lipeng Chen}, + title = {{Accelerating Molecular Vibrational Spectra Simulations with a + Physically Informed Deep Learning Model}}, + journal = {J. Chem. Theory Comput.}, + year = 2024, + volume = 20, + number = 11, + pages = {4703--4710}, + doi = {10.1021/acs.jctc.4c00173}, + abstract = {In recent years, machine learning (ML) surrogate models have emerged + as an indispensable tool to accelerate simulations of physical and + chemical processes. However, there is still a lack of ML models that + can accurately predict molecular vibrational spectra. Here, we present + a highly efficient multitask ML surrogate model termed Vibrational + Spectra Neural Network (VSpecNN), to accurately calculate infrared + (IR) and Raman spectra based on dipole moments and polarizabilities + obtained on-the-fly via ML-enhanced molecular dynamics simulations. + The methodology is applied to pyrazine, a prototypical polyatomic + chromophore. The VSpecNN-predicted energies are well within the + chemical accuracy (1 kcal/mol), and the errors for VSpecNN-predicted + forces are only half of those obtained from a popular high-performance + ML model. Compared to the ab initio reference, the VSpecNN-predicted + frequencies of IR and Raman spectra differ only by less than 5.87 + cm-1, and the intensities of IR spectra and the depolarization ratios + of Raman spectra are well reproduced. The VSpecNN model developed in + this work highlights the importance of constructing highly accurate + neural network potentials for predicting molecular vibrational + spectra.}, +} + +@Article{Du_ChemMater_2024_v36_p6167, + author = {Tao Du and Xuan Ge and Fengming Cao and Han Liu and Caijuan Shi and + Junwei Ding and Daming Sun and Qiangqiang Zhang and Yuanzheng Yue and + Morten M. Smedskjaer}, + title = {{Structural Origin of the Deformation Propensity of Zeolitic + Imidazolate Framework Glasses}}, + journal = {Chem. Mater.}, + year = 2024, + volume = 36, + number = 12, + pages = {6167--6179}, + doi = {10.1021/acs.chemmater.4c00921}, +} + +@Article{Bhullar_ChemphyschemEurJChemPhysPhysChem_2024_pe202400090, + author = {Mangladeep Bhullar and Zihao Bai and Akinwumi Akinpelu and Yansun Yao}, + title = {{Phase Transition in Silicon from Machine Learning Informed + Metadynamics}}, + journal = {Chemphyschem: Eur. J. Chem. Phys. Phys. Chem.}, + year = 2024, + pages = {e202400090}, + doi = {10.1002/cphc.202400090}, + abstract = {Investigating reconstructive phase transitions in large-sized systems + requires a highly efficient computational framework with computational + cost proportional to the system size. Traditionally, widely used + frameworks such as density functional theory (DFT) have been + prohibitively expensive for extensive simulations on large systems + that require long-time scales. To address this challenge, this study + employed well-trained machine learning potential to simulate phase + transitions in a large-size system. This work integrates the + metadynamics simulation approach with machine learning potential, + specifically deep potential, to enhance computational efficiency and + accelerate the study of phase transition and consequent development of + grains and dislocation defects in a system. The new method is + demonstrated using the phase transitions of bulk silicon under high + pressure. This approach has revealed the transition path and formation + of polycrystalline silicon systems under specific stress conditions, + demonstrating the effectiveness of deep potential-driven metadynamics + simulations in gaining insights into complex material behaviors in + large-sized systems.}, +} + +@Article{Zhang_CeramInt_2024_v50_p30008, + author = {Meng Zhang and Siyan Deng and Jianghong Zhang and Daxin Li and Dechang + Jia and Yu Zhou}, + title = {{The structural evolution of SiBCNZr amorphous ceramics analyzed by + machine learning potential}}, + journal = {Ceram. Int.}, + year = 2024, + volume = 50, + number = 17, + pages = {30008--30017}, + doi = {10.1016/j.ceramint.2024.05.296}, +} + +@Article{Gong_AngewChemInternationalEngl_2024_v63_pe202405379, + author = {Fu-Qiang Gong and Yun-Pei Liu and Ye Wang and Weinan E and Zhong-Qun + Tian and Jun Cheng}, + title = {{Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on + Catalysis through Surface Pre-melting of Nanoclusters}}, + journal = {Angew. Chem. (International, Engl.)}, + year = 2024, + volume = 63, + number = 27, + pages = {e202405379}, + doi = {10.1002/anie.202405379}, + abstract = {Due to the superior catalytic activity and efficient utilization of + noble metals, nanocatalysts are extensively used in the modern + industrial production of chemicals. The surface structures of these + materials are significantly influenced by reactive adsorbates, leading + to dynamic behavior under experimental conditions. The dynamic nature + poses significant challenges in studying the structure-activity + relations of catalysts. Herein, we unveil an anomalous entropic effect + on catalysis via surface pre-melting of nanoclusters through machine + learning accelerated molecular dynamics and free energy calculation. + We find that due to the pre-melting of shell atoms, there exists a + non-linear variation in the catalytic activity of the nanoclusters + with temperature. Consequently, two notable changes in catalyst + activity occur at the respective temperatures of melting for the shell + and core atoms. We further study the nanoclusters with surface point + defects, i.e. vacancy and ad-atom, and observe significant decrease in + the surface melting temperatures of the nanoclusters, enabling the + reaction to take place under more favorable and milder conditions. + These findings not only provide novel insights into dynamic catalysis + of nanoclusters but also offer new understanding of the role of point + defects in catalytic processes.}, +} + +@Article{Feng_BrazJChemEng_2024, + author = {Taixi Feng and Zhaoting Liu and Guimin Lu}, + title = {{Deep potential molecular dynamic and electrochemical experiments to + reveal the structure and behavior of Mn(II) in magnesium electrolysis}}, + journal = {Braz. J. Chem. Eng.}, + year = 2024, + doi = {10.1007/s43153-024-00465-9}, +} + +@Article{Wang_AdvFunctMater_2024, + author = {Lei Wang and Zehua Gao and Kerong Su and Nhat Truong Nguyen and + Rui{-}Ting Gao and Junxiang Chen and Lei Wang}, + title = {{Stacked High{-}Entropy Hydroxides Promote Charge Transfer Kinetics for + Photoelectrochemical Water Splitting}}, + journal = {Adv Funct Mater.}, + year = 2024, + doi = {10.1002/adfm.202403948}, + abstract = {AbstractAlthough + various kinds of cocatalyst are developed and decorated on the bismuth + vanadate (BiVO4{\ensuremath{<} + }/jats:sub>) photoanode, its photoelectrochemical (PEC) + water splitting performance is limited owing to severe charge + recombination and sluggish oxygen evolution reaction (OER). Herein, a + high{-}entropy hydroxide electrocatalyst (FeCoNiMoCrOOH) is + constructed as a co{-}catalyst deposited on BiVO4 with a + good PEC activity and stability in potassium borate buffer, addressing + substantial charge recombination and poor surface oxygen evolution + reaction of the material. FeCoNiMoCrOOH synthesized by a simple + electrodeposition stacking strategy, delivers an overpotential of + 172{~}mV at 10{~}mA{~}cm{\ensu + remath{-}}2 with a stability + of 600{~}h under alkaline conditions, representing one of the best + performances on high{-}entropy{-}based catalysts. The FeCoNiMoCrOOH/Bi + VO4}} photoanode shows a photocurrent density of 5.23{~}mA{~} + cm{\ensuremath{- + }}2 at 1.23 V< + jats:sub>RHE + with 100{~}h durability in potassium borate buffer. Experimental + investigations and theoretical calculations demonstrate that the + synergistic effect of Mo and Cr in FeCoNi catalyst effectively + decreases the dissolution Fe, Co, and Ni after long{-}term operation, + increases the charge transfer kinetics, and promotes OER and PEC + performances, therefore enhancing the photocorrosion resistance of BiV + O4}}. This work provides a new avenue to design high + entropy{-}based electrocatalysts boosting solar water splitting + activity and stability.}, +} + +@Article{Fazel_JComputChem_2024_v45_p1821, + author = {Kamron Fazel and Nima Karimitari and Tanooj Shah and Christopher + Sutton and Ravishankar Sundararaman}, + title = {{Improving the reliability of machine learned potentials for modeling + inhomogeneous liquids}}, + journal = {J. Comput. Chem.}, + year = 2024, + volume = 45, + number = 21, + pages = {1821--1828}, + doi = {10.1002/jcc.27353}, + abstract = {The atomic-scale response of inhomogeneous fluids at interfaces and + surrounding solute particles plays a critical role in governing + chemical, electrochemical, and biological processes. Classical + molecular dynamics simulations have been applied extensively to + simulate the response of fluids to inhomogeneities directly, but are + limited by the accuracy of the underlying interatomic potentials. + Here, we use neural network potentials (NNPs) trained to ab initio + simulations to accurately predict the inhomogeneous responses of two + distinct fluids: liquid water and molten NaCl. Although NNPs can be + readily trained to model complex bulk systems across a range of state + points, we show that to appropriately model a fluid's response at an + interface, relevant inhomogeneous configurations must be included in + the training data. In order to sufficiently sample appropriate + configurations of such inhomogeneous fluids, we develop protocols + based on molecular dynamics simulations in the presence of external + potentials. We demonstrate that NNPs trained on inhomogeneous fluid + configurations can more accurately predict several key properties of + fluids-including the density response, surface tension and size- + dependent cavitation free energies-for liquid water and molten NaCl, + compared to both empirical interatomic potentials and NNPs that are + not trained on such inhomogeneous configurations. This work therefore + provides a first demonstration and framework to extract the response + of inhomogeneous fluids from first principles for classical density- + functional treatment of fluids free from empirical potentials.}, +} + +@Article{Yang_AdvEnergyMater_2024, + author = {Xiaochen Yang and Sunny Gupta and Yu Chen and Dogancan Sari and + Han{-}Ming Hau and Zijian Cai and Chaochao Dun and Miao Qi and Lu Ma + and Yi Liu and Jeffrey J. Urban and Gerbrand Ceder}, + title = {{Fast Room{-}Temperature Mg{-}Ion Conduction in Clay{-}Like Halide + Glassy Electrolytes}}, + journal = {Adv. Energy Mater.}, + year = 2024, + doi = {10.1002/aenm.202400163}, + abstract = {AbstractThe + discovery of mechanically soft solid{-}state materials with fast + Mg{-}ion conduction is crucial for the development of solid{-}state + magnesium batteries. In this paper, novel magnesium gallium halide + compounds are reported that achieve high ionic conductivity of 0.47 mS + {~}cm{\ensuremath{- + }}1 at room temperature. + These Mg{-}ion conductors obtained by ball milling Mg and Ga salts + exhibit clay{-}like mechanical properties, enabling intimate contact + at the electrode{\textendash}electrolyte interface during battery + cycling. With a combination of experimental and computational + analysis, this study identifies that the soft{-}clay formation is + induced by partial anion exchange during milling. This partial anion + exchange creates undercoordinated magnesium ions in a chlorine{-}rich + environment, yielding fast Mg{-}ion conduction. This work demonstrates + the potential of clay{-}like halide electrolytes for + all{-}solid{-}state magnesium batteries, with possible further + extension to other multivalent battery + systems.}, +} + +@Article{XU_SciSinTech_2024_v54_p477, + author = {ShanSen XU and Jian CHANG and Bin ZHAI and PengXu YAN and MaoJie LIN + and BingBo WEI}, + title = {{Amorphous solidification mechanism of multicomponent Ti-Cu based alloy + investigated by molecular dynamics simulation and drop tube + experiments}}, + journal = {Sci. Sin.-Tech.}, + year = 2024, + volume = 54, + number = 3, + pages = {477--489}, + doi = {10.1360/SST-2023-0408}, +} + +@Article{Hu_AdvFunctMater_2024, + author = {Taiping Hu and Linhan Xu and Fuzhi Dai and Guobing Zhou and Fangjia Fu + and Xiaoxu Wang and Linsen Li and Xinping Ai and Shenzhen Xu}, + title = {{Impact of Amorphous LiF Coating Layers on Cathode{-}Electrolyte + Interfaces in Solid{-}State Batteries}}, + journal = {Adv Funct Mater.}, + year = 2024, + doi = {10.1002/adfm.202402993}, + abstract = {AbstractHigh + interfacial resistance between electrodes and solid{-}state + electrolyte is the major cause for the failure of all{-}solid{-}state + Li{-}ion batteries. Spontaneous (electro)chemical reactions and poor + Li{-}ion diffusion at the interfaces are closely related to this + increased impedance. Although introducing a coating layer can mitigate + interfacial reactions and structural reconstruction, it may also lead + to poor Li{-}ion diffusion. Balancing this trade{-}off therefore is + crucial for the design of coating layer materials. In this study, the + impact of the amorphous LiF (a{-}LiF) coating layer on interfacial + structural reconstruction and Li{-}ion diffusion at the LiCoO{\ensurem + ath{<}}jats:sub>2}}/Li6PS5{\ensurem + ath{<}}/jats:sub>Cl solid{-}state interface is + explicitly elucidated{~}via machine{-}learning{-}assisted molecular + dynamics (MD) simulations. It is found that the a{-}LiF can + effectively protect the P{-}S tetrahedron local structures in Li{\ensu + remath{<}}jats:sub>6}}PS5Cl but cannot suppress the formation of side + product S2 dimers. It is further discovered that once the + a{-}LiF coating exceeds a certain critical thickness, emergence of + ordered local structures will inhibit Li{-}ion diffusion. The + simulations propose that the optimal thickness of the coating layer is + around 1{~}nm. Overall, the work provides a microscopic understanding + for effects of the a{-}LiF coating layer on the structural and kinetic + properties of cathode{-}solid electrolyte interfaces and can guide the + design of interfacial coating materials for solid{-}state + batteries.}, +} + +@Article{Pikalova_DoklPhysChem_2024_v514_p9, + author = {N. S. Pikalova and I. A. Balyakin and A. A. Yuryev and A. A. Rempel}, + title = {{Prediction of Mechanical Properties of High-Entropy Carbide + (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C with the Use of Machine Learning + Potential}}, + journal = {Dokl Phys Chem}, + year = 2024, + volume = 514, + number = 1, + pages = {9--14}, + doi = {10.1134/S0012501624600049}, +} + +@Article{Luo_AdvEngMater_2024, + author = {Kun Luo and Xiao Han and Jonathan Cappola and Dian Li and Yufeng Zheng + and Lin Li and Feng Yan and Qi An}, + title = {{Hyper{-}Elastic Deformation via Martensitic Phase Transformation in + Cadmium Telluride}}, + journal = {Adv Eng Mater}, + year = 2024, + doi = {10.1002/adem.202302076}, + abstract = {Cadmium telluride (CdTe) is a + highly promising material for photovoltaics (PV) and photodetectors + due to its light{-}absorbing properties. However, efficient design and + use of flexible devices require a deep understanding of its + atomic{-}level deformation mechanism. Herein, uniaxial compression + deformation of CdTe monocrystalline with varying crystal orientations + is investigated using molecular dynamics (MD) with a newly developed + machine{-}learning force field (ML{-}FF), alongside in{-}situ + micropillar compression experiments. The findings reveal that CdTe + bulk deformation is dominated by reversible martensitic phase + transformation, whereas CdTe pillar deformation is primarily driven by + dislocation nucleation and movement. CdTe monocrystals possess + exceptional super{-}recoverable deformation along the + {\&}lt;100{\&}gt; orientation due to hyper{-}elastic processes induced + by martensitic transformation. This discovery not only sheds light on + the peculiarities observed in micropillar experimental measurements, + but also provides pivotal insights into the fundamental deformation + behaviors of CdTe and similar II{\textendash}VI compounds under + various stress conditions. These insights are crucial for the + innovative design and enhanced functionality of future flexible + electronic devices.}, +} + +@Article{Luo_PhysRevRes_2024_v6_p13292, + author = {Chenxing Luo and Yang Sun and Renata M. Wentzcovitch}, + title = {{Probing the state of hydrogen in $\delta$-AlOOH + at mantle conditions with machine + learning potential}}, + journal = {Phys, Rev, Res.}, + year = 2024, + volume = 6, + number = 1, + pages = 13292, + doi = {10.1103/PhysRevResearch.6.013292}, + abstract = {Hydrous and nominally anhydrous minerals (NAMs) are a fundamental + class of solids of enormous significance to geophysics. They are the + water carriers in the deep geological water cycle and impact + structural, elastic, plastic, and thermodynamic properties and phase + relations in Earth's forming aggregates (rocks). They play a critical + role in the geochemical and geophysical processes that shape the + planet. Their complexity has prevented predictive calculations of + their properties, but progress in materials simulations ushered by + machine learning potentials is transforming this state of affairs. + Here, we adopt a hybrid approach that combines deep learning + potentials (DP) with the SCAN meta-GGA functional to simulate a + prototypical hydrous system. We illustrate the success of this + approach to simulate {\$}{\textbackslash}delta{\$}-AlOOH + ({\$}{\textbackslash}delta{\$}), a phase capable of transporting water + down to near the core-mantle boundary of the Earth + ({\textasciitilde}2,900 km depth and {\textasciitilde}135 GPa) in + subducting slabs. A high-throughput sampling of phase space using + molecular dynamics simulations with DP-potentials sheds light on the + hydrogen-bond behavior and proton diffusion at geophysical conditions. + These simulations provide a pathway for a deeper understanding of + these crucial components that shape Earth's internal state.}, +} + +@Article{Chen_SmallWeinheimBergstrGer_2024_pe2400083, + author = {Weiqi Chen and Kang Wang and Xinran Miao and Jie Zhang and Aisheng + Song and Xinchun Chen and Jianbin Luo and Tianbao Ma}, + title = {{Ultralow-Friction at Cryogenic Temperature Induced by Hydrogen + Correlated Quantum Effect}}, + journal = {Small (Weinheim Bergstr. Ger.)}, + year = 2024, + pages = {e2400083}, + doi = {10.1002/smll.202400083}, + abstract = {Temperature is one of the governing factors affecting friction of + solids. Undesired high friction state has been generally reported at + cryogenic temperatures due to the prohibition of thermally activated + processes, following conventional Arrhenius equation. This has brought + huge difficulties to lubrication at extremely low temperatures in + industry. Here, the study uncovers a hydrogen-correlated sub-Arrhenius + friction behavior in hydrogenated amorphous carbon (a-C:H) film at + cryogenic temperatures, and a stable ultralow-friction over a wide + temperature range (103-348 K) is achieved. This is attributed to + hydrogen-transfer-induced mild structural ordering transformation, + confirmed by machine-learning-based molecular dynamics simulations. + The anomalous sub-Arrhenius temperature dependence of structural + ordering transformation rate is well-described by a quantum mechanical + tunneling (QMT) modified Arrhenius model, which is correlated with + quantum delocalization of hydrogen in tribochemical reactions. This + work reveals a hydrogen-correlated friction mechanism overcoming the + Arrhenius temperature dependence and provides a new pathway for + achieving ultralow friction under cryogenic conditions.}, +} + +@Article{Pegolo_FrontMater_2024_v11, + author = {Paolo Pegolo and Federico Grasselli}, + title = {{Thermal transport of glasses via machine learning driven simulations}}, + journal = {Front., Mater,}, + year = 2024, + volume = 11, + doi = {10.3389/fmats.2024.1369034}, + abstract = {Accessing the thermal transport + properties of glasses is a major issue for the design of production + strategies of glass industry, as well as for the plethora of + applications and devices where glasses are employed. From the + computational standpoint, the chemical and morphological complexity of + glasses calls for atomistic simulations where the interatomic + potentials are able to capture the variety of local environments, + composition, and (dis)order that typically characterize glassy phases. + Machine-learning potentials (MLPs) are emerging as a valid alternative + to computationally expensive + ab + initio simulations, + inevitably run on very small samples which cannot account for disorder + at different scales, as well as to empirical force fields, fast but + often reliable only in a narrow portion of the thermodynamic and + composition phase diagrams. In this article, we make the point on the + use of MLPs to compute the thermal conductivity of glasses, through a + review of recent theoretical and computational tools and a series of + numerical applications on vitreous silica and vitreous silicon, both + pure and intercalated with + lithium.}, +} + +@Article{Wen_PhysChemChemPhys_2024_v26_p9984, + author = {Mingjie Wen and Xiaoya Chang and Yabei Xu and Dongping Chen and + Qingzhao Chu}, + title = {{Determining the mechanical and decomposition properties of high + energetic materials ({\ensuremath{\alpha}}-RDX, + {\ensuremath{\beta}}-HMX, and {\ensuremath{\varepsilon}}-CL-20) using + a neural network potential}}, + journal = {Phys. Chem. Chem. Phys.}, + year = 2024, + volume = 26, + number = 13, + pages = {9984--9997}, + doi = {10.1039/d4cp00017j}, + abstract = {Molecular simulations of high energetic materials (HEMs) are limited + by efficiency and accuracy. Recently, neural network potential (NNP) + models have achieved molecular simulations of millions of atoms while + maintaining the accuracy of density functional theory (DFT) levels. + Herein, an NNP model covering typical HEMs containing C, H, N, and O + elements is developed. The mechanical and decomposition properties of + 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), + hexahydro-1,3,5-trinitro-1,3,5-triazine (HMX), and + 2,4,6,8,10,12-hexanitrohexaazaisowurtzitane (CL-20) are determined by + employing the molecular dynamics (MD) simulations based on the NNP + model. The calculated results show that the mechanical properties of + {\ensuremath{\alpha}}-RDX, {\ensuremath{\beta}}-HMX, and + {\ensuremath{\varepsilon}}-CL-20 agree with previous experiments and + theoretical results, including cell parameters, equations of state, + and elastic constants. In the thermal decomposition simulations, it is + also found that the initial decomposition reactions of the three + crystals are N-NO2 homolysis, corresponding radical intermediates + formation, and NO2-induced reactions. This decomposition trajectory is + mainly divided into two stages separating from the peak of NO2: + pyrolysis and oxidation. Overall, the NNP model for C/H/N/O elements + in this work is an alternative reactive force field for RDX, HMX, and + CL-20 HEMs, and it opens up new potential for future kinetic study of + nitramine explosives.}, +} + +@Article{BinJassar_AdvFunctMater_2024, + author = {Mohammed {\{}Bin Jassar{\}} and Carine Michel and Sara Abada and + Theodorus {\{}De Bruin{\}} and Sylvain Tant and Carlos Nieto{-}Draghi + and Stephan N. Steinmann}, + title = {{A Perspective on the Molecular Modeling of Electrolyte Decomposition + Reactions for Solid Electrolyte Interphase Growth in Lithium{-}Ion + Batteries}}, + journal = {Adv Funct Mater.}, + year = 2024, + doi = {10.1002/adfm.202313188}, + abstract = {AbstractThe solid + electrolyte interphase (SEI) is a thin heterogeneous layer formed at + the anode/electrolyte interface in lithium{-}ion batteries as a + consequence of the reduction of the electrolyte. The initial formation + of the SEI inhibits the direct contact between the electrode and the + electrolyte and thus protects the battery. However, the composition, + structure, and size of the SEI evolve over time and the growth of the + SEI is considered the primary mechanism leading to the gradual + deterioration of the battery performance. Despite the importance of + the SEI and its growth, the atomistic understanding of the underlying + elementary reaction steps remains partial. Molecular modeling of the + electrolyte decomposition is key to gain detailed insights that are + complementary to experiments for the reactions occurring in this + heterogenous interphase. In this perspective, the electron transport + mechanisms are first described from the anode to the electrolyte + through the SEI and highlight the importance of the inorganic/organic + interface within the heterogeneous SEI: it is where the electrolyte + decomposition reactions are likely to occur. Finally, a view is + provided on the current progress on molecular modeling techniques + (e.g., Density Functional Theory, force fields, machine learning + potentials) of the SEI and the challenges each method + faces.}, +} + +@Article{Xu_AdvFunctMater_2024_v34, + author = {Bo Xu and Zhanpeng Gong and Jingran Liu and Yunfei Hong and Yang Yang + and Lou Li and Yilun Liu and Junkai Deng and Jefferson Zhe Liu}, + title = {{Tunable Ferroelectric Topological Defects on 2D Topological Surfaces: + Complex Strain Engineering Skyrmion{-}Like Polar Structures in 2D + Materials}}, + journal = {Adv Funct Mater.}, + year = 2024, + volume = 34, + number = 26, + doi = {10.1002/adfm.202311599}, + abstract = {AbstractPolar + topological structures in ferroelectric materials have attracted + significant interest due to their fascinating physical properties and + promising applications in high{-}density, nonvolatile memories. + Currently, most polar topological patterns are only observed in the + bulky perovskite superlattices. In this work, a discovery of tunable + ferroelectric polar topological structures is reported, designed, and + achieved using topological strain engineering in two{-}dimensional + (2D) PbX (X = S, Se, and Te) materials via integrating + first{-}principles calculations, machine learning molecular dynamics + simulations, and continuum modeling.{~}First{-}principles calculations + discover the strain{-}induced reversible ferroelectric phase + transition with diverse polarization directions strongly correlated to + the straining conditions. Taking advantage of the mechanical + flexibility of 2D PbX, using molecular dynamics (MD) simulations, it + is successfully demonstrated that the complex strain fields of 2D + topological surfaces under mechanical indentation can generate unique + skyrmion{-}like polar topological vortex patterns. Further continuum + simulations for experimentally accessible larger{-}scale 2D + topological surfaces uncover multiple skyrmion{-}like structures + (i.e., vortex, anti{-}vortex, and flux{-}closure) and transition + between them by adopting/designing different types of mechanical + loadings (such as out{-}of{-}plane indention and air blowing). + Topological surfaces with various designable reversible polar + topological structures can be tailored by complex straining flexible + 2D materials, which provides excellent opportunities for + next{-}generation nanoelectronics and + sensor{~}devices.}, +} + +@Article{Bodenschatz_MaterBaselSwitz_2024_v17_p286, + author = {Cameron J. Bodenschatz and Wissam A. Saidi and Jamesa L. Stokes and + Rebekah I. Webster and Gustavo Costa}, + title = {{Theoretical Prediction of Thermal Expansion Anisotropy for Y2Si2O7 + Environmental Barrier Coatings Using a Deep Neural Network Potential + and Comparison to Experiment}}, + journal = {Mater. (Basel Switz.)}, + year = 2024, + volume = 17, + number = 2, + pages = 286, + doi = {10.3390/ma17020286}, + abstract = {Environmental barrier coatings (EBCs) are an enabling technology for + silicon carbide (SiC)-based ceramic matrix composites (CMCs) in + extreme environments such as gas turbine engines. However, the + development of new coating systems is hindered by the large design + space and difficulty in predicting the properties for these materials. + Density Functional Theory (DFT) has successfully been used to model + and predict some thermodynamic and thermo-mechanical properties of + high-temperature ceramics for EBCs, although these calculations are + challenging due to their high computational costs. In this work, we + use machine learning to train a deep neural network potential (DNP) + for Y2Si2O7, which is then applied to calculate the thermodynamic and + thermo-mechanical properties at near-DFT accuracy much faster and + using less computational resources than DFT. We use this DNP to + predict the phonon-based thermodynamic properties of Y2Si2O7 with good + agreement to DFT and experiments. We also utilize the DNP to calculate + the anisotropic, lattice direction-dependent coefficients of thermal + expansion (CTEs) for Y2Si2O7. Molecular dynamics trajectories using + the DNP correctly demonstrate the accurate prediction of the + anisotropy of the CTE in good agreement with the diffraction + experiments. In the future, this DNP could be applied to accelerate + additional property calculations for Y2Si2O7 compared to DFT or + experiments.}, +} + +@Article{You_MaterTodayPhys_2024_v40_p101282, + author = {Jiang You and Cheng Wang and Qi Wang and Min Zha and Hai-Long Jia and + Jian Wang and Hui-Yuan Wang}, + title = {{Accumulative coarse-graining of simple structural descriptors enables + accurately predicting the dynamics of metallic liquids}}, + journal = {Mater. Today Phys.}, + year = 2024, + volume = 40, + pages = 101282, + doi = {10.1016/j.mtphys.2023.101282}, +} + +@Article{Kamaeva_JMolLiq_2024_v393_p123659, + author = {L.V. Kamaeva and E.N. Tsiok and N.M. Chtchelkatchev}, + title = {{Deep machine learning, molecular dynamics and experimental studies of + liquid Al-Cu-Co alloys}}, + journal = {J. Mol. Liq.}, + year = 2024, + volume = 393, + pages = 123659, + doi = {10.1016/j.molliq.2023.123659}, +} + +@Article{Jiang_JNuclMater_2024_v588_p154824, + author = {Peng Jiang and Rongyang Qiu and Jinli Cao and Xichuan Liao and + Yangchun Chen and Zhixiao Liu and Xinfu He and Wen Yang and Huiqiu + Deng}, + title = {{Development of U-Zr-Xe ternary interatomic potentials appropriate for + simulation of defect and Xe behaviors in U-Zr system}}, + journal = {J. Nucl. Mater.}, + year = 2024, + volume = 588, + pages = 154824, + doi = {10.1016/j.jnucmat.2023.154824}, +} + +@Article{Li_IntJHeatMassTransf_2024_v225_p125404, + author = {Zhiqiang Li and Haoyu Dong and Jian Wang and Linhua Liu and Jia-Yue + Yang}, + title = {{Active learning molecular dynamics-assisted insights into ultralow + thermal conductivity of two-dimensional covalent organic frameworks}}, + journal = {Int. J. Heat Mass Transf.}, + year = 2024, + volume = 225, + pages = 125404, + doi = {10.1016/j.ijheatmasstransfer.2024.125404}, +} + +@Article{Benayad_ProcNatlAcadSciUSA_2024_v121_pe2322040121, + author = {Zakarya Benayad and Rolf David and Guillaume Stirnemann}, + title = {{Prebiotic chemical reactivity in solution with quantum accuracy and + microsecond sampling using neural network potentials}}, + journal = {Proc. Natl. Acad. Sci. U. S. A.}, + year = 2024, + volume = 121, + number = 23, + pages = {e2322040121}, + doi = {10.1073/pnas.2322040121}, + abstract = {While RNA appears as a good candidate for the first autocatalytic + systems preceding the emergence of modern life, the synthesis of RNA + oligonucleotides without enzymes remains challenging. Because the + uncatalyzed reaction is extremely slow, experimental studies bring + limited and indirect information on the reaction mechanism, the nature + of which remains debated. Here, we develop neural network potentials + (NNPs) to study the phosphoester bond formation in water. While NNPs + are becoming routinely applied to nonreactive systems or simple + reactions, we demonstrate how they can systematically be trained to + explore the reaction phase space for complex reactions involving + several proton transfers and exchanges of heavy atoms. We then + propagate at moderate computational cost hundreds of nanoseconds of a + variety of enhanced sampling simulations with quantum accuracy in + explicit solvent conditions. The thermodynamically preferred reaction + pathway is a concerted, dissociative mechanism, with the transient + formation of a metaphosphate transition state and direct participation + of water solvent molecules that facilitate the exchange of protons + through the nonbridging phosphate oxygens. Associative-dissociative + pathways, characterized by a much tighter pentacoordinated phosphate, + are higher in free energy. Our simulations also suggest that + diprotonated phosphate, whose reactivity is never directly assessed in + the experiments, is significantly less reactive than the + monoprotonated species, suggesting that it is probably never the + reactive species in normal pH conditions. These observations + rationalize unexplained experimental results and the temperature + dependence of the reaction rate, and they pave the way for the design + of more efficient abiotic catalysts and activating groups.}, +} + +@Article{Xing_ChemEngJ_2024_v489_p151492, + author = {Zhihao Xing and Xi Jiang}, + title = {{Neural network potential-based molecular investigation of pollutant + formation of ammonia and ammonia-hydrogen combustion}}, + journal = {Chem. Eng. J.}, + year = 2024, + volume = 489, + pages = 151492, + doi = {10.1016/j.cej.2024.151492}, +} + +@Article{Zhou_CemConcrRes_2024_v180_p107501, + author = {Ao Zhou and Juntao Kang and Renyuan Qin and Huali Hao and Tiejun Liu + and Zechuan Yu}, + title = {{Weaving the next-level structure of calcium silicate hydrate at the + submicron scale via a remapping algorithm from coarse-grained to all- + atom model}}, + journal = {Cem. Concr. Res.}, + year = 2024, + volume = 180, + pages = 107501, + doi = {10.1016/j.cemconres.2024.107501}, +} + +@Article{Pelaez_JChemTheoryComput_2024_v20_p4076, + author = {Raul P. Pelaez and Guillem Simeon and Raimondas Galvelis and Antonio + Mirarchi and Peter Eastman and Stefan Doerr and Philipp Th{\"o}lke and + Thomas E. Markland and Gianni {\{}De Fabritiis{\}}}, + title = {{TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular + Simulations}}, + journal = {J. Chem. Theory Comput.}, + year = 2024, + volume = 20, + number = 10, + pages = {4076--4087}, + doi = {10.1021/acs.jctc.4c00253}, + abstract = {Achieving a balance between computational speed, prediction accuracy, + and universal applicability in molecular simulations has been a + persistent challenge. This paper presents substantial advancements in + TorchMD-Net software, a pivotal step forward in the shift from + conventional force fields to neural network-based potentials. The + evolution of TorchMD-Net into a more comprehensive and versatile + framework is highlighted, incorporating cutting-edge architectures + such as TensorNet. This transformation is achieved through a modular + design approach, encouraging customized applications within the + scientific community. The most notable enhancement is a significant + improvement in computational efficiency, achieving a very remarkable + acceleration in the computation of energy and forces for TensorNet + models, with performance gains ranging from 2{\texttimes} to + 10{\texttimes} over previous, nonoptimized, iterations. Other + enhancements include highly optimized neighbor search algorithms that + support periodic boundary conditions and smooth integration with + existing molecular dynamics frameworks. Additionally, the updated + version introduces the capability to integrate physical priors, + further enriching its application spectrum and utility in research. + The software is available at https://github.com/torchmd/torchmd-net.}, +} + +@Article{Villot_JChemPhys_2024_v160_p184103, + author = {Corentin Villot and Ka Un Lao}, + title = {{Ab initio dispersion potentials based on physics-based functional + forms with machine learning}}, + journal = {J. Chem. Phys.}, + year = 2024, + volume = 160, + number = 18, + pages = 184103, + doi = {10.1063/5.0204064}, + abstract = {In this study, we introduce SAPT10K, a comprehensive dataset + comprising 9982 noncovalent interaction energies and their binding + energy components (electrostatics, exchange, induction, and + dispersion) for diverse intermolecular complexes of 944 unique dimers. + These complexes cover significant portions of the intermolecular + potential energy surface and were computed using higher-order + symmetry-adapted perturbation theory, SAPT2+(3)(CCD), with a large + aug-cc-pVTZ basis set. The dispersion energy values in SAPT10K serve + as crucial inputs for refining the ab{~}initio dispersion potentials + based on Grimme's D3 and many-body dispersion (MBD) models. + Additionally, {\ensuremath{\Delta}} machine learning (ML) models based + on newly developed intermolecular features, which are derived from + intermolecular histograms of distances for element/substructure pairs + to simultaneously account for local environments as well as long-range + correlations, are also developed to address deficiencies of the D3/MBD + models, including the inflexibility of their functional forms, the + absence of MBD contributions in D3, and the standard Hirshfeld + partitioning scheme used in MBD. The developed dispersion models can + be applied to complexes involving a wide range of elements and charged + monomers, surpassing other popular ML models, which are limited to + systems with only neutral monomers and specific elements. The + efficient D3-ML model, with Cartesian coordinates as the sole input, + demonstrates promising results on a testing set comprising 6714 + dimers, outperforming another popular ML model, component-based + machine-learned intermolecular force field (CLIFF), by 1.5 times. + These refined D3/MBD-ML models have the capability to replace the + time-consuming dispersion components in symmetry-adapted perturbation + theory-based calculations and can promptly illustrate the dispersion + contribution in noncovalent complexes for supramolecular assembly and + chemical reactions.}, +} + +@Article{Zhong_JChemPhys_2024_v160_p124303, + author = {Shenghui Zhong and Zheyu Shi and Bin Zhang and Zhengcheng Wen and + Longfei Chen}, + title = {{Homogeneous water vapor condensation with a deep neural network + potential model}}, + journal = {J. Chem. Phys.}, + year = 2024, + volume = 160, + number = 12, + pages = 124303, + doi = {10.1063/5.0189448}, + abstract = {Molecular-level nucleation has not been clearly understood due to the + complexity of multi-body potentials and the stochastic, rare nature of + the process. This work utilizes molecular dynamics (MD) simulations, + incorporating a first-principles-based deep neural network (DNN) + potential model, to investigate homogeneous water vapor condensation. + The nucleation rates and critical nucleus sizes predicted by the DNN + model are compared against commonly used semi-empirical models, namely + extended simple point charge (SPC/E), TIP4P, and OPC, in addition to + classical nucleation theory (CNT). The nucleation rates from the DNN + model are comparable with those from the OPC model yet surpass the + rates from the SPC/E and TIP4P models, a discrepancy that could mainly + arise from the overestimated bulk free energy by SPC/E and TIP4P. The + surface free energy predicted by CNT is lower than that in MD + simulations, while its bulk free energy is higher than that in MD + simulations, irrespective of the potential model used. Further + analysis of cluster properties with the DNN model unveils pronounced + variations of O-H bond length and H-O-H bond angle, along with + averaged bond lengths and angles that are enlarged during embryonic + cluster formation. Properties such as cluster surface free energy and + liquid-to-vapor density transition profiles exhibit significant + deviations from CNT assumptions.}, +} + +@Article{Hodapp_ComputMaterSci_2024_v233_p112715, + author = {M. Hodapp}, + title = {{Machine learning is funny but physics makes the money: How machine- + learning potentials can advance computer-aided materials design in + metallurgy}}, + journal = {Comput. Mater. Sci.}, + year = 2024, + volume = 233, + pages = 112715, + doi = {10.1016/j.commatsci.2023.112715}, +} + +@Article{Ding_JPhysChemLett_2024_v15_p616, + author = {Ye Ding and Jing Huang}, + title = {{DP/MM: A Hybrid Model for Zinc-Protein Interactions in Molecular + Dynamics}}, + journal = {J. Phys. Chem. Lett.}, + year = 2024, + volume = 15, + number = 2, + pages = {616--627}, + doi = {10.1021/acs.jpclett.3c03158}, + abstract = {Zinc-containing proteins are vital for many biological processes, yet + accurately modeling them using classical force fields is hindered by + complicated polarization and charge transfer effects. This study + introduces DP/MM, a hybrid force field scheme that utilizes a deep + potential model to correct the atomic forces of zinc ions and their + coordinated atoms, elevating them from MM to QM levels of accuracy. + Trained on the difference between MM and QM atomic forces across + diverse zinc coordination groups, the DP/MM model faithfully + reproduces structural characteristics of zinc coordination during + simulations, such as the tetrahedral coordination of Cys4 and Cys3His1 + groups. Furthermore, DP/MM allows water exchange in the zinc + coordination environment. With its unique blend of accuracy, + efficiency, flexibility, and transferability, DP/MM serves as a + valuable tool for studying structures and dynamics of zinc-containing + proteins and also represents a pioneering approach in the evolving + landscape of machine learning potentials for molecular modeling.}, +} + +@Article{Liao_BiophysJ_2024_pS0006-34952400163-2, + author = {Jun Liao and Mincong Wu and Junyong Gao and Changjun Chen}, + title = {{Calculation of solvation force in molecular dynamics simulation by + deep-learning method}}, + journal = {Biophys. J.}, + year = 2024, + pages = {S0006-3495(24)00163-2}, + doi = {10.1016/j.bpj.2024.02.029}, + abstract = {Electrostatic calculations are generally used in studying the + thermodynamics and kinetics of biomolecules in solvent. Generally, + this is performed by solving the Poisson-Boltzmann equation on a large + grid system, a process known to be time consuming. In this study, we + developed a deep neural network to predict the decomposed solvation + free energies and forces of all atoms in a molecule. To train the + network, the internal coordinates of the molecule were used as the + input data, and the solvation free energies along with transformed + atomic forces from the Poisson-Boltzmann equation were used as labels. + Both the training and prediction tasks were accelerated on GPU. Formal + tests demonstrated that our method can provide reasonable predictions + for small molecules when the network is well-trained with its + simulation data. This method is suitable for processing lots of + snapshots of molecules in a long trajectory. Moreover, we applied this + method in the molecular dynamics simulation with enhanced sampling. + The calculated free energy landscape closely resembled that obtained + from explicit solvent simulations.}, +} + +@Article{Chen_MatterRadiatExtrem_2024_v9, + author = {Tao Chen and Qianrui Liu and Yu Liu and Liang Sun and Mohan Chen}, + title = {{Combining stochastic density functional theory with deep potential + molecular dynamics to study warm dense matter}}, + journal = {Matter Radiat. Extrem.}, + year = 2024, + volume = 9, + number = 1, + doi = {10.1063/5.0163303}, + abstract = {{\ensuremath{<}}jats:p{\ensuremath{>}}In traditional finite- + temperature Kohn{\textendash}Sham density functional theory (KSDFT), + the partial occupation of a large number of high-energy KS eigenstates + restricts the use of first-principles molecular dynamics methods at + extremely high temperatures. However, stochastic density functional + theory (SDFT) can overcome this limitation. Recently, SDFT and the + related mixed stochastic{\textendash}deterministic density functional + theory, based on a plane-wave basis set, have been implemented in the + first-principles electronic structure software ABACUS [Q. Liu and M. + Chen, Phys. Rev. B 106, 125132 (2022)]. In this study, we combine SDFT + with the Born{\textendash}Oppenheimer molecular dynamics method to + investigate systems with temperatures ranging from a few tens of eV to + 1000{~}eV. Importantly, we train machine-learning-based interatomic + models using the SDFT data and employ these deep potential models to + simulate large-scale systems with long trajectories. Subsequently, we + compute and analyze the structural properties, dynamic properties, and + transport coefficients of warm dense + matter.{\ensuremath{<}}/jats:p{\ensuremath{>}}}, +} diff --git a/source/papers/deepmd-kit/index.md b/source/papers/deepmd-kit/index.md index 6510c7d..e4df816 100644 --- a/source/papers/deepmd-kit/index.md +++ b/source/papers/deepmd-kit/index.md @@ -1,7 +1,7 @@ --- title: Publications driven by DeePMD-kit date: 2022-05-01 -update: 2023-11-28 +update: 2024-06-30 mathjax: true --- @@ -9,10 +9,158 @@ The following publications have used the DeePMD-kit software. Publications that We encourage explicitly mentioning DeePMD-kit with proper citations in your publications, so we can more easily find and list these publications. -Last update date: Nov 28, 2023 +Last update date: June 30, 2024 ## 2024 {% publications %} +Chen_MatterRadiatExtrem_2024_v9, +Zhang_AcsPhysChemAu_2024, +Zhang_NatCommun_2024_v15_p4223, +Hedman_NatCommun_2024_v15_p4076, +Owen_NpjComputMater_2024_v10_p92, +Fan_NatCommun_2024_v15_p3251, +Li_NatCommun_2024_v15_p2653, +AbouElKheir_NpjComputMater_2024_v10_p33, +You_NpjComputMater_2024_v10_p57, +Qi_NpjComputMater_2024_v10_p43, +Wieser_NpjComputMater_2024_v10_p18, +DeAngelis_SciRep_2024_v14_p978, +Zhang_JColloidInterfaceSci_2024_v671_p258, +Wang_ComputMaterSci_2024_v244_p113154, +Bhatt_IntJHeatMassTransf_2024_v229_p125673, +Wang_NanoEnergy_2024_v127_p109762, +Feng_SolEnergyMaterSolCells_2024_v272_p112903, +Galvani_JPhysMater_2024_v7_p35003, +Che_CeramInt_2024_v50_p22865, +Zhang_JMaterSciTechnol_2024_v185_p23, +Wang_ComposBEng_2024_v279_p111452, +Hua_EnergyStorageMater_2024_v70_p103470, +Chen_WaterRes_2024_v256_p121580, +Cao_IntJHeatMassTransf_2024_v224_p125359, +Shi_JAmCeramSoc_2024_v107_p3845, +Zhang_JEurCeramSoc_2024_v44_p4243, +Sowa_JPhysChemC_2024_v128_p8724, +Wang_JAmChemSoc_2024_v146_p14566, +Liu_JApplPhys_2024_v135, +Yokaichiya_JChemPhys_2024_v160_p204108, +David_JAmChemSoc_2024_v146_p14213, +Que_JChemPhys_2024_v160_p194710, +Guo_JChemPhys_2024_v160_p174313, +Omranpour_JChemPhys_2024_v160_p170901, +Zhang_AcsMaterLett_2024_v6_p1849, +Shi_JPhysChemA_2024_v128_p3449, +Selvaraj_JElectrochemSoc_2024_v171_p50544, +Li_PhysRevB_2024_v109_p184108, +Shi_PhysRevB_2024_v109_p174104, +Zhang_PhysRevB_2024_v109_p174106, +Shi_ExtremMechLett_2024_v68_p102151, +Xiao_BioresourTechnol_2024_v399_p130590, +Mirchi_PhysChemChemPhys_2024_v26_p14216, +Dong_JApplPhys_2024_v135, +Balyakin_ComputMaterSci_2024_v239_p112979, +Ghaffari_ComputMaterSci_2024_v239_p112983, +Zhu_ComputMaterSci_2024_v239_p112966, +Zhu_JPhysChemLett_2024_v15_p4024, +Zills_JPhysChemB_2024_v128_p3662, +Zhou_AcsApplMaterInterfaces_2024_v16_p18874, +Zhang_InorgChem_2024_v63_p6743, +Woo_MaterLett_2024_v361_p136114, +Feng_ChemEngSci_2024_v288_p119836, +Zhang_CeramInt_2024_v50_p13740, +Pan_JComputChem_2024_v45_p638, +Chen_JApplPhys_2024_v135, +Zhai_JChemPhys_2024_v160_p144501, +Maxson_JPhysChemLett_2024_v15_p3740, +Miyagawa_JMaterChemA_2024_v12_p11344, +Ying_AcsNano_2024_v18_p10133, +Zhang_ApplPhysLett_2024_v124, +Xu_JPhysChemC_2024_v128_p5697, +Kobayashi_ChemSci_2024_v15_p6816, +Hsing_PhysRevMater_2024_v8_p43806, +Agrawal_Nanoscale_2024_v16_p8986, +Peng_JgrSolidEarth_2024_v129, +Du_NatlSciRev_2024_v11_pnwae023, +Prasnikar_ArtifIntellRev_2024_v57_p102, +Zhang_SciChinaMater_2024_v67_p1129, +Fu_JNuclMater_2024_v591_p154897, +Obeid_JEnergyStorage_2024_v82_p110587, +Xu_JApplPhys_2024_v135, +Chang_AcsApplMaterInterfaces_2024_v16_p14954, +Li_PhysChemChemPhys_2024_v26_p12044, +Liu_ChemMaterPublAmChemSoc_2024_v36_p2898, +delaPuente_JPhysChemLett_2024_v15_p3096, +Xiao_PhysChemChemPhys_2024_v26_p11867, +Peng_GeophysResLett_2024_v51, +Pang_PhysChemChemPhys_2024_v26_p11545, +Gan_PhysRevB_2024_v109_p115129, +Soshnikov_JChemPhys_2024_v160_p094117, +Liu_JPhysChemA_2024_v128_p1656, +Ojih_JMaterChemA_2024_v12_p8502, +Ren_EnergyEnvSci_2024_v17_p2743, +Liu_ChemSci_2024_v15_p5294, +Urata_PhysRevMater_2024_v8_p33602, +Wan_PhysRevB_2024_v109_p94101, +Ryu_JMolLiq_2024_v397_p124054, +Gardner_MachLearnSciTechnol_2024_v5_p15003, +Liu_GeosciFront_2024_v15_p101735, +Yang_Arxiv_2024_v135, +Zhang_ComputMaterSci_2024_v235_p112843, +Bertani_JChemTheoryComput_2024_v20_p1358, +BinFaheem_JPhysChemC_2024_v128_p2163, +Guo_AcsCatal_2024_v14_p1232, +Gong_PhysRevB_2024_v109_p54117, +Zhang_PhysRevAppl_2024_v21_p24043, +Ding_IntJMolSci_2024_v25_p1448, +Uporov_Intermetallics_2024_v165_p108121, +Kamath_JPhysChemA_2024_v128_p807, +Zhai_ApplPhysA_2024_v130_p106, +Guo_PhysChemChemPhys_2024_v26_p6590, +Yang_ChemSci_2024_v15_p3382, +Li_ComputMaterSci_2024_v232_p112656, +Ju_ComputMaterSci_2024_v232_p112664, +Wang_PhysChemChemPhys_2024_v26_p6351, +Zhang_CellRepPhysSci_2024_v5_p101760, +Fan_Nanoscale_2024_v16_p3438, +Feng_JMolLiq_2024_v394_p123533, +Thakur_JChemPhys_2024_v160_p024502, +Kussainova_JChemEngData_2024_v69_p204, +Dong_AcsApplMaterInterfaces_2024_v16_p530, +Hedelius_JChemTheoryComput_2024_v20_p199, +Kwon_AcsApplMaterInterfaces_2024_v16_p31687, +Raman_MolPhys_2024, +Urata_JAmCeramSoc_2024, +Chen_JChemTheoryComput_2024_v20_p4703, +Du_ChemMater_2024_v36_p6167, +Bhullar_ChemphyschemEurJChemPhysPhysChem_2024_pe202400090, +Zhang_CeramInt_2024_v50_p30008, +Gong_AngewChemInternationalEngl_2024_v63_pe202405379, +Feng_BrazJChemEng_2024, +Wang_AdvFunctMater_2024, +Fazel_JComputChem_2024_v45_p1821, +Yang_AdvEnergyMater_2024, +XU_SciSinTech_2024_v54_p477, +Hu_AdvFunctMater_2024, +Pikalova_DoklPhysChem_2024_v514_p9, +Luo_AdvEngMater_2024, +Luo_PhysRevRes_2024_v6_p13292, +Chen_SmallWeinheimBergstrGer_2024_pe2400083, +Pegolo_FrontMater_2024_v11, +Wen_PhysChemChemPhys_2024_v26_p9984, +BinJassar_AdvFunctMater_2024, +Xu_AdvFunctMater_2024_v34, +Bodenschatz_MaterBaselSwitz_2024_v17_p286, +You_MaterTodayPhys_2024_v40_p101282, +Kamaeva_JMolLiq_2024_v393_p123659, +Jiang_JNuclMater_2024_v588_p154824, +Li_IntJHeatMassTransf_2024_v225_p125404, +Benayad_ProcNatlAcadSciUSA_2024_v121_pe2322040121, +Xing_ChemEngJ_2024_v489_p151492, +Zhou_CemConcrRes_2024_v180_p107501, +Villot_JChemPhys_2024_v160_p184103, +Zhong_JChemPhys_2024_v160_p124303, +Hodapp_ComputMaterSci_2024_v233_p112715, +Ding_JPhysChemLett_2024_v15_p616, +Liao_BiophysJ_2024_pS0006-34952400163-2, He_ActaMaterialia_2024_v262_p119416, Shang_Fuel_2024_v357_p129909, Li_JournaloftheEuropeanCeramicSociety_2024_v44_p659 @@ -20,6 +168,31 @@ Li_JournaloftheEuropeanCeramicSociety_2024_v44_p659 ## 2023 {% publications %} +Xu_JPhysChemC_2023_v127_p24106, +Wu_JPhysChemLett_2023_v14_p11465, +Hu_CellRepPhysSci_2023_v4_p101713, +Loose_JPhysChemB_2023_v127_p10564, +Bai_NanoLett_2023_v23_p10922, +Shayestehpour_JChemTheoryComput_2023_v19_p8732, +He_PhysRevB_2023_v108_p224114, +Crippa_MachLearnSciTechnol_2023_v4_p45044, +San_NatCommun_2023_v14_p7858, +Zeng_NpjComputMater_2023_v9_p213, +deVilla_NatCommun_2023_v14_p7580, +Guo_JChemPhys_2023_v159_p204702, +Lim_JPhysChemC_2023_v127_p22692, +delaPuente_JAmChemSoc_2023_v145_p25186, +Xiao_JApplPhys_2023_v134, +Gromoff_Nanoscale_2023_v16_p384, +Sun_Macromolecules_2023_v56_p9003, +Chen_JChemTheoryComput_2023_v19_p7861, +QiuQiu_ChinPhysLett_2023_v40_p116301, +Wu_PhysRevB_2023_v108_pL180104, +Lei_PhysRevB_2023_v108_p184105, +Bhatt_PhysRevMater_2023_v7_p115001, +Gong_PhysRevB_2023_v108_p134112, +Bonati_ProcNatlAcadSciUSA_2023_v120_pe2313023120, +Liu_PhysRevE_2023_v108_p55310, Thong_PhysRevB_2023_v107_p014101, Zhang_PhysChemChemPhys_2023_v25_p13297, Li_JournalofSustainableCementBasedMaterials_2023_v12_p1335, @@ -323,6 +496,7 @@ Yue_JChemPhys_2021_v154_p034111 ## 2020 {% publications %} +Marcolongo_Chemsystemschem_2020_v2, CalegariAndrade_ChemSci_2020_v11_p2335, Dai_JournalofMaterialsScienceTechnology_2020_v43_p168, Li_ApplPhysLett_2020_v117_p152102, diff --git a/source/papers/dpgen/index.md b/source/papers/dpgen/index.md index f0774d7..528cd83 100644 --- a/source/papers/dpgen/index.md +++ b/source/papers/dpgen/index.md @@ -1,7 +1,7 @@ --- title: Publications driven by DP-GEN date: 2022-05-11 -update: 2023-11-28 +update: 2024-06-30 mathjax: true --- @@ -9,16 +9,26 @@ The following publications have used the DP-GEN software. Publications that only We encourage explicitly mentioning DP-GEN with proper citations in your publications, so we can more easily find and list these publications. -Last update date: Nov 28, 2023 +Last update date: June 30, 2024 ## 2024 {% publications %} +Bhullar_ChemphyschemEurJChemPhysPhysChem_2024_pe202400090, +Shi_ExtremMechLett_2024_v68_p102151, +Shi_PhysRevB_2024_v109_p174104, +Galvani_JPhysMater_2024_v7_p35003, +Zhang_AcsPhysChemAu_2024, He_ActaMaterialia_2024_v262_p119416, Shang_Fuel_2024_v357_p129909 {% endpublications %} ## 2023 {% publications %} +Liu_PhysRevE_2023_v108_p55310, +Wu_PhysRevB_2023_v108_pL180104, +Shayestehpour_JChemTheoryComput_2023_v19_p8732, +Bai_NanoLett_2023_v23_p10922, +Xu_JPhysChemC_2023_v127_p24106, Thong_PhysRevB_2023_v107_p014101, Zhang_PhysChemChemPhys_2023_v25_p13297, Ding_Tungsten_2023_vNone_pNone,