Machine Learned Potentials for In2O3

Lead Research Organisation: University of Cambridge
Department Name: Chemistry

Abstract

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.

Publications

10 25 50

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