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.
Open Positions
- No openings at the moment. PhD candidates will be considered from October 2024 (for admission in October 2025).
Research Themes
Fundamentals
- Inverse design of materials using artificial intelligence
- 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 chalcogenide semiconductors (e.g. Cu2ZnSnS4, AgBiS2)
- Electroactive metal-organic frameworks (e.g. Fe2(DSBDC), Cu3(HHTP)2)
Community Projects
- Open computational materials science Nature Materials
- 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
- 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.