Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt.

ABSTRACT: Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous "on-the-fly" training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H₂ turnover on

SUBMITTER: Vandermause J 

PROVIDER: S-EPMC9440250 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature