Atomic Cluster Expansion Force Fields for Molecules

Machine Learning based force fields have revolutionised the modelling of materials at the atomistic scale. In this talk I will describe Atomic Cluster Expansion (ACE) which provides a systematic framework to derive a formally complete set of symmetric polynomial basis functions that can be used to build highly accurate and fast force fields. I will demonstrate that ACE force fields parametrised using regularised linear regression can compete in accuracy with most Gaussian Process and Neural Network based approaches. In particular, I will describe several applications of ACE to molecular systems where it shows excellent smooth and physical extrapolation to unseen parts of the Potential Energy Surface. Finally, I will demonstrate how the ACE framework can be extended to provide a unifying framework for machine learning potentials which includes message passing neural networks like SchNet and NequIP, Behler-Parinallo neural networks as well as the Gaussian Process regression based SOAP-GAP approaches.