  Accurate modeling of chemically reactive systems has traditionally relied on
either expensive ab initio approaches or flexible bond-order force fields such
as ReaxFF that require considerable time, effort, and expertise to
parameterize. Here, we introduce FLARE++, a Bayesian active learning method for
training reactive many-body force fields on the fly during molecular dynamics
(MD) simulations. During the automated training loop, the predictive
uncertainties of a sparse Gaussian process (SGP) force field are evaluated at
each timestep of an MD simulation to determine whether additional ab initio
data are needed. Once trained, the SGP is mapped onto an equivalent and much
faster model that is polynomial in the local environment descriptors and whose
prediction cost is independent of the training set size. We apply our method to
a canonical reactive system in the field of heterogeneous catalysis, hydrogen
splitting and recombination on a platinum (111) surface, obtaining a trained
model within three days of wall time that is twice as fast as a recent Pt/H
ReaxFF force field and considerably more accurate. Our method is fully open
source and is expected to reduce the time and effort required to train fast and
accurate reactive force fields for complex systems.
