Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
This method improves neural network potentials for molecular and materials systems by enabling more efficient active learning, though it is incremental as it builds on existing uncertainty quantification and adversarial attack techniques.
The paper tackles the problem of efficiently sampling uncertain regions in neural network interatomic potentials by using differentiable adversarial attacks on uncertainty metrics, which expands the training domain and reduces the number of calls to ground truth methods.
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.