Active learning of reactive Bayesian force fields: Application to heterogeneous catalysis dynamics of H/Pt

Atomistic modeling of chemically reactive systems has traditionally relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we introduce FLARE++, a Bayesian active learning method for training reactive many-body force fields on the fly during molecular dynamics (MD) simulations. At each timestep, the predictive uncertainties of a sparse Gaussian process (SGP) are evaluated to automatically determine whether additional ab initio data are needed. The resulting SGP is mapped onto a polynomial model whose prediction cost is independent of the training set size. Using this method, we perform large-scale MD simulations of a prototypical system in heterogeneous catalysis---H2 chemisorption on Pt(111)---at chemical accuracy. The model, trained within three days of wall time, performs at twice the simulation speed of an available ReaxFF model while maintaining ab initio accuracy to a much higher fidelity. Our method enables efficient automated training of fast and accurate reactive force fields for chemically complex systems.