  Neural network (NN) interatomic potentials provide fast prediction of
potential energy surfaces, closely matching the accuracy of the electronic
structure methods used to produce the training data. However, NN predictions
are only reliable within well-learned training domains, and show volatile
behavior when extrapolating. Uncertainty quantification approaches can flag
atomic configurations for which prediction confidence is low, but arriving at
such uncertain regions requires expensive sampling of the NN phase space, often
using atomistic simulations. Here, we exploit automatic differentiation to
drive atomistic systems towards high-likelihood, high-uncertainty
configurations without the need for molecular dynamics simulations. By
performing adversarial attacks on an uncertainty metric, informative geometries
that expand the training domain of NNs are sampled. When combined to an active
learning loop, this approach bootstraps and improves NN potentials while
decreasing the number of calls to the ground truth method. This efficiency is
demonstrated on sampling of kinetic barriers and collective variables in
molecules, and can be extended to any NN potential architecture and materials
system.
