A unified Bayesian approach to learning many-body potentials

Machine learned (ML) interatomic potentials have emerged as a powerful tool for performing large-scale molecular dynamics simulations at near-DFT accuracy, but training many-body ML potentials that are interpretable, efficient, and uncertainty-aware remains an important open challenge. In this talk, we present Bayesian force fields that unite three frameworks—the Atomic Cluster Expansion (ACE), Gaussian Approximation Potentials (GAP), and Spectral Neighbor Analysis Potentials (SNAP)—opening the door to scalable, uncertainty-aware molecular dynamics simulations of complex materials. We use a multi-species generalization of the N-body ACE descriptor to define a tunable many-body kernel for sparse Gaussian process (GP) regression, and show that mean predictions of the GP can be mapped onto equivalent and much faster models resembling SNAP and qSNAP models. The Bayesian error bars provided by the sparse GP make it possible to train force fields on the fly during molecular dynamics, and we apply this automated training protocol to model phase transitions in the shape memory alloy nickel titanium.