Navigating Materials Space with Computers
We are an academic group working 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 at Imperial College London.
Research Themes
Fundamentals
- Inverse design of materials with predetermined properties
- Crystal thermodynamics and phase transformations
- Ion, electron, and phonon transport in the solid state
Applications
- Crystal engineering for clean energy technologies
- Photochemistry of solar cells
- Electrochemical energy storage and fuel production
Materials
- Metal halide perovskites (e.g. CH3NH3PbI3, Cs3Bi2Br9)
- Multi-component chalcogenides (e.g. Cu2ZnSnS4, CuBiS2)
- Electroactive metal-organic frameworks (e.g. Fe2(DSBDC), Cu3(HHTP)2)
Community Projects
- 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
- Materials by design roadmap J Phys D
- Best practices in characterization of perovskite-inspired photovoltaics Chemistry of Materials
Contact
If you are interested in collaborating or joining the group, please get in touch by e-mail.