High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks

TL;DR

This paper introduces a novel Monte Carlo Markov chain sampling algorithm and an equivariant Bayesian neural network model for interatomic force prediction, achieving high accuracy and uncertainty quantification.

Contribution

The paper presents a new MCMC sampling method and a Bayesian neural network architecture tailored for interatomic force modeling, addressing previous computational challenges.

Findings

Achieves state-of-the-art accuracy in force prediction.

Provides reliable uncertainty estimates.

Demonstrates improved convergence of sampling algorithms.

Abstract

Even though Bayesian neural networks offer a promising framework for modeling uncertainty, active learning and incorporating prior physical knowledge, few applications of them can be found in the context of interatomic force modeling. One of the main challenges in their application to learning interatomic forces is the lack of suitable Monte Carlo Markov chain sampling algorithms for the posterior density, as the commonly used algorithms do not converge in a practical amount of time for many of the state-of-the-art architectures. As a response to this challenge, we introduce a new Monte Carlo Markov chain sampling algorithm in this paper which can circumvent the problems of the existing sampling methods. 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 good measure of uncertainty.