Neural Network Potentials

We integrate quantum chemical topology with machine learning to build physically motivated neural network interatomic potentials and to accelerate the discovery of new chemical bonding descriptors.

A tool for the automated generation of optimised Atom-Centred Symmetry Functions (ACSFs) for neural network interatomic potentials, using Gaussian Mixture Models to characterise the chemical space of a system.