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 at the atomic scale 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

  • For postdoctoral candidates, the Marie Curie Fellowships is open, while the next call for Newton International Fellowships will be in early 2026.

  • For PhD candidates, the best time to get in touch with your CV and motivation is in October-December each year, for admission in the following academic year.

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
  • 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

Contact

If you are interested in collaborating or joining the group, please get in touch by e-mail.