This package integrates the powerful AIMNet2 neural network potential into your simulation workflows. AIMNet2 provides fast and reliable energy, force, and property calculations for molecules containing a diverse range of elements.
- Accurate and Versatile: AIMNet2 excels at modeling neutral, charged, organic, and elemental-organic systems.
- Flexible Interfaces: Use AIMNet2 through convenient calculators for popular simulation packages like ASE and PySisyphus.
- Flexible Long-Range Interactions: Optionally employ the Dumped-Shifted Force (DSF) or Ewald summation Coulomb models for accurate calculations in large or periodic systems.
While package is in alpha stage and repository is private, please install into your conda envoronment manually with
# install requirements
conda install -y pytorch pytorch-cuda=12.1 -c pytorch -c nvidia
conda install -y -c pyg pytorch-cluster
conda install -y -c conda-forge openbabel ase
## pysis requirements
conda install -y -c conda-forge autograd dask distributed h5py fabric jinja2 joblib matplotlib numpy natsort psutil pyyaml rmsd scipy sympy scikit-learn
# now should not do any pip installs
pip install git+https://github.com/eljost/pysisyphus.git
# finally, this repo
git clone [email protected]:zubatyuk/aimnet2calc.git
cd aimnet2calc
python setup.py install
from aimnet2calc import AIMNet2ASE
calc = AIMNet2ASE('aimnet2')
To specify total molecular charge and spin multiplicity, use optional charge and mult keyword arguments, or set_charge and set_mult methods:
calc = AIMNet2ASE('aimnet2', charge=1)
atoms1.calc = calc
# calculations on atoms1 will be done with charge 1
....
atoms2.calc = calc
calc.set_charge(-2)
# calculations on atoms1 will be done with charge -2
PySisyphus [https://pysisyphus.readthedocs.io]
from aimnet2calc import AIMNet2PySis
calc = AIMNet2PySis('aimnet2')
This produces standard PySisyphus calculator.
Instead of Pysis command line utility, use aimnet2pysis. This registeres AIMNet2 calculator with PySisyphus.
Example calc section for PySisyphus YAML files:
calc:
type: aimnet # use AIMNet2 calculator
model: aimnet2_b973c # use aimnet2_b973c_0.jpt model
from aimnet2calc import AIMNet2Calculator
calc = AIMNet2Calculator('aimnet2')
will load default AIMNet2 model aimnet2_wb97m_0.jpt as defined at aimnet2calc/models.py . If file does not exist on the machine, it will be downloaded from aimnet-model-zoo repository.
calc = AIMNet2Calculator('/path/to_a/model.jpt')
will load model from the file.
The calculator accepts a dictionary containig lists, numpy arrays, torch tensors, or anything that could be accepted by torch.as_tensor.
The input could be for a single molecule (dict keys and shapes):
coord: (B, N, 3) # atomic coordinates in Angstrom
numbers (B, N) # atomic numbers
charge (B,) # molecular charge
mult (B,) # spin multiplicity, optional
or for a concatenation of molecules:
coord: (N, 3) # atomic coordinates in Angstrom
numbers (N,) # atomic numbers
charge (B,) # molecular charge
mult (B,) # spin multiplicity, optional
mol_idx (N,) # molecule index for each atom, should contain integers in increasing order, with (B-1) is the maximum number.
where B is the number of molecules, N is number of atoms.
results = calc(data, forces=False, stress=False, hessian=False)
results would be a dictionary of PyTorch tensors containing energy, charges, and possibly forces, stress and hessian if requested.
By default, Coulomb energy is calculated in O(N^2) manner, e.g. pair interaction between every pair of atoms in system. For very large or periodic systems, O(N) Dumped-Shifted Force Coulomb model could be employed doi: 10.1063/1.2206581. With AIMNet2Calculator interface, switch between standard and DSF Coulomb implementations im AIMNet2 models:
# switch to O(N)
calc.set_lrcoulomb_method('dsf', cutoff=15.0, dsf_alpha=0.2)
# switch to O(N^2), not suitable for PBC
calc.set_lrcoulomb_method('simple')