Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

TL;DR

This paper introduces a differentiable, adversarial attack-based method to efficiently sample uncertain molecular geometries, enhancing neural network potentials and reducing reliance on expensive simulations.

Contribution

It presents a novel automatic differentiation approach to generate high-uncertainty configurations without molecular dynamics, improving training data for neural network potentials.

Findings

Efficient sampling of uncertain geometries improves NN potential training.

Reduces number of expensive ground truth evaluations.

Applicable to various molecular systems and NN architectures.

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 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.