Machine Learned Potentials for In2O3

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Year 1: Generic training activities for all first-year student members of the CDT.Year 2-4: Recent progress in constructing machine learned potentials for molecular and solid systems has redefined the boundaries of computational chemistry and solid-state physics at the atomic scale. However, simulations of part molecular part solid systems, such as those required for heterogeneous catalysis, have remained challenging. Here we will fit a Gaussian Approximation Potentials (GAP) for Indium Oxide as a test model. This project will set the stepping stones to test chemically relevant observables, investigate molecular surface interactions and sets the path to an all encompassing potential for heterogeneous catalysis.