Towards Automated and Robust Atomic Cluster Expansion Models

The Atomic Cluster Expansion (ACE) is a general and systematic linear model for representing interatomic potentials and force fields. In this presentation, I will present our latest efforts to automate the selection of training sets, feature selection, and parameter estimation via a combination of uncertainty quantification, a variant on active-learning, robust regression techniques and injecting modelling insight (geometric priors) into the model formulation. Show-case applications include fully automated generation of phase diagrams for multi-component alloys. Time permitting I will discuss early attempts to explain the impressive generalisation capabilities of ACE (and MLIPs in general).