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

• We have a new postdoctoral opening from October 2024 in the area of materials informatics for renewable energy applications, involving aspects of computational chemistry, physics and machine learning. For more information and application details see here.

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 chalcogenides (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.