Related papers: Fast Uncertainty Estimates in Deep Learning Interatomic Potentials

Fast Uncertainty Estimates in Deep Learning Interatomic Potentials

URL: http://arxiv.org/abs/2211.09866v1
Date: Thu, 17 Nov 2022 20:13:39 GMT
Title: Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
Authors: Albert Zhu, Simon Batzner, Albert Musaelian, Boris Kozinsky
Abstract summary: We propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of independently trained neural networks. This incurs a large computational overhead in both training and prediction that often results in order-of-magnitude more expensive predictions. Here, we propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost.