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.
- We will have several PhD positions open for admission in October 2024 in the area of materials informatics for renewable energy applications, involving aspects of computational chemistry, physics and machine learning. Please e-mail with your CV for further details.
- 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 chalcogenides (e.g. Cu2ZnSnS4, AgBiS2)
- Electroactive metal-organic frameworks (e.g. Fe2(DSBDC), Cu3(HHTP)2)
- 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
If you are interested in collaborating or joining the group, please get in touch by e-mail.