Providing Machine Learning Potentials with High Quality Uncertainty Estimates

TL;DR

This paper introduces Bayesian Neural Networks for machine learning potentials in computational chemistry, providing high-quality uncertainty estimates that enable more reliable and efficient hybrid workflows compared to traditional ensemble methods.

Contribution

The work presents a novel application of Bayesian Neural Networks for uncertainty quantification in machine-learned potentials, improving upon ensemble-based approaches.

Findings

BNNs offer more principled uncertainty estimates.

Enhanced hybrid workflows for computational chemistry.

Resource-efficient uncertainty quantification.

Abstract

Computational chemistry has come a long way over the course of several decades, enabling subatomic level calculations particularly with the development of Density Functional Theory (DFT). Recently, machine-learned potentials (MLP) have provided a way to overcome the prevalent time and length scale constraints in such calculations. Unfortunately, these models utilise complex and high dimensional representations, making it challenging for users to intuit performance from chemical structure, which has motivated the development of methods for uncertainty quantification. One of the most common methods is to introduce an ensemble of models and employ an averaging approach to determine the uncertainty. In this work, we introduced Bayesian Neural Networks (BNNs) for uncertainty aware energy evaluation as a more principled and resource efficient method to achieve this goal. The richness of our uncertainty quantification enables a new type of hybrid workflow where calculations can be offloaded to a MLP in a principled manner.