Uncertainty estimation for molecular dynamics and sampling

TL;DR

This paper discusses methods for estimating uncertainty in machine learning-based molecular dynamics simulations, enhancing reliability and enabling active learning for more accurate thermodynamic predictions across diverse systems.

Contribution

It introduces an on-the-fly reweighing scheme for uncertainty estimation and demonstrates its application to various systems, improving simulation resilience and accuracy.

Findings

Uncertainty quantification improves simulation reliability.

The on-the-fly reweighing scheme accurately estimates thermodynamic uncertainties.

Applications to water and liquid gallium validate the approach.

Abstract

Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale and complexity. Given the interpolative nature of these models, the reliability of predictions depends on the position in phase space, and it is crucial to obtain an estimate of the error that derives from the finite number of reference structures included during the training of the model. When using a machine-learning potential to sample a finite-temperature ensemble, the uncertainty on individual configurations translates into an error on thermodynamic averages, and provides an indication for the loss of accuracy when the simulation enters a previously unexplored region. Here we discuss how uncertainty quantification can be used, together with a baseline energy model, or a more robust although less accurate interatomic potential, to obtain more resilient simulations and to support active-learning strategies. Furthermore, we introduce an on-the-fly reweighing scheme that makes it possible to estimate the uncertainty in the thermodynamic averages extracted from long trajectories. We present examples covering different types of structural and thermodynamic properties, and systems as diverse as water and liquid gallium.