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Navigating Materials Space with Computers |
We are an academic group at Imperial College London focused on the design and optimisation of advanced materials using high-performance computing. Our research on computational materials chemistry combines quantum mechanics with data-driven machine learning and multi-scale modelling approaches. The group is led by Professor Aron Walsh in the Thomas Young Centre for the Theory & Simulation of Molecules & Materials and the Centre for Processable Electronics.
- There are no current openings. For postdoctoral candidates, the next call for Newton Intertational Fellowships will be in Janauary 2025.
- Inverse design of materials using artificial intelligence
- Crystal thermodynamics and phase transformations
- Ion, electron, and phonon transport in the solid state
- Crystal engineering for clean energy technologies
- Photochemistry of solar cells
- Electrochemical energy storage and fuel production
- Metal halide perovskites (e.g. CH3NH3PbI3, Cs3Bi2Br9)
- Multi-component chalcogenide semiconductors (e.g. Cu2ZnSnS4, AgBiS2)
- Electroactive metal-organic frameworks (e.g. Fe2(DSBDC), Cu3(HHTP)2)
- Open computational materials science Nature Materials
- Roadmap on established and emerging photovoltaics for sustainable energy conversion J Phys Energy
- Best practices in machine learning for chemistry Nature Chemistry
- Emerging inorganic solar cell efficiency tables J Phys Energy
- Stability assessment and reporting for perovskite photovoltaics Nature Energy
If you are interested in collaborating or joining the group, please get in touch by e-mail.