Machine Learned Potentials for In2O3

Lead Research Organisation: University of Cambridge
Department Name: Chemistry


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.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024220/1 31/05/2019 30/11/2027
2468419 Studentship EP/S024220/1 30/09/2020 29/09/2024 Lars Schaaf