Electrocatalysis is central to decarbonising the chemical industry, from carbon dioxide reduction to green hydrogen production. Most computational tools in this area were developed for aqueous systems, but the field is now moving towards non-aqueous electrolytes such as ionic liquids and organic carbonates, which offer new reaction pathways and stability windows. Our group has recently implemented an implicit solvent model which interfaces with a density functional theory code. This project will parameterise it using data from machine learning interatomic potentials. The result will be a fast, accurate method for modelling solvent effects in electrocatalysis, trained against high-fidelity atomistic simulations.

This project is well suited for a chemical engineer who wants applied, method-driven research. You will gain skills in electronic structure methods and machine learning interatomic potentials, while keeping the work focussed in practical outcomes for catalysis. This project is a good fit if you want a PhD that combines quantum chemistry, data science and thermodynamics. For more information, please visit the group webpage.