Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks.

ABSTRACT: 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 methods 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, info

SUBMITTER: Schwalbe-Koda D 

PROVIDER: S-EPMC8384857 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature