Active learning of fast Bayesian force fields with mapped gaussian processes - application to stability of stanene

Machine learning force-fields can reach accuracy comparable to ab-initio molecular dynamics and simulate much larger systems. Gaussian process (GP) regression has remarkable advantage due to its built-in uncertainty quantification based on Bayesian posterior inference, which can be used to monitor the quality of predictions. A limitation is that the prediction cost grows linearly with the training set size, making accurate GP predictions slow. To solve this, we exploit the special structure of an n-body kernel function to construct interpolation functions based on the trained GP, mapping both forces and uncertainties. To demonstrate the capability of this mapped GP Bayesian force field (BFF) method, we perform active learning and large-scale simulation of stanene. We reveal the decomposition mechanism of stanene and identify the range the phase transition temperature. The application shows that we can reach classical potential prediction speed while keeping quantum accuracy, at the same time incorporating uncertainty quantification. We present progress in implementing automated active learning workflows for training BFFs, aimed at large-scale simulations of rare event dynamics in complex materials.