High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks

Abstract

Ab initio molecular dynamics simulations of material properties have become a cornerstone in the development of novel materials for a wide range of applications such as battery technology and catalysis. Unfortunately, their high computational demand can make them unsuitable in many applications. Consequently, surrogate modeling via neural networks has become an active field of research. Two of the major obstacles to their practical application in many cases are assessing the reliability of the neural network predictions and the difficulty of generating suitable datasets to train the neural network in the first place. Bayesian neural networks offer a promising framework for modeling uncertainty, active learning and improving data efficiency and robustness by incorporating prior physical knowledge. However, due to the high computational demand and slow convergence of the gold standard approach of Monte Carlo Markov Chain (MCMC) sampling methods, variational inference via Monte Carlo dropout is currently the only sampling method successfully applied in this domain. Since MCMC methods have often displayed a superior quality in their uncertainty quantification, developing a suitable MCMC method in this domain would be a significant advance in making neural network-based molecular dynamics simulations more practically viable. In this paper, we demonstrate that convergence for state-of-the-art models with high-quality MCMC methods can still be achieved in a practical amount of time by introducing a novel parameter-specific adaptive step size scheme. In addition, we introduce a new stochastic neural network model based on the NequIP architecture and demonstrate that, when combined with our novel sampling algorithm, we obtain predictions with state-of-the-art accuracy as well as a significantly improved measure of uncertainty over Monte Carlo dropout. Lastly, we show that the proposed algorithm can even outperform deep ensembles while sampling from a single Markov chain.