Efficient Generation of Stable Linear Machine-Learning Force Fields with Uncertainty-Aware Active Learning

TL;DR

This paper presents an active-learning approach using linear regression to efficiently generate accurate and stable machine-learning force fields with minimal ab initio data, suitable for molecular dynamics simulations.

Contribution

It introduces an uncertainty-aware active-learning strategy based on linear models that requires only tens of ab initio calculations to produce reliable force fields.

Findings

Only tens of ab initio simulations needed for stable force fields.

Method achieves near chemical accuracy in molecular dynamics.

No conformational pre-sampling required, minimal user intervention.

Abstract

Machine-learning force fields enable an accurate and universal description of the potential energy surface of molecules and materials on the basis of a training set of ab initio data. However, large-scale applications of these methods rest on the possibility to train accurate machine learning models with a small number of ab initio data. In this respect, active-learning strategies, where the training set is self-generated by the model itself, combined with linear machine-learning models are particularly promising. In this work, we explore an active-learning strategy based on linear regression and able to predict the model's uncertainty on predictions for molecular configurations not sampled by the training set, thus providing a straightforward recipe for the extension of the latter. We apply this strategy to the spectral neighbor analysis potential and show that only tens of ab initio simulations of atomic forces are required to generate stable force fields for room-temperature molecular dynamics at or close to chemical accuracy. Moreover, the method does not necessitate any conformational pre-sampling, thus requiring minimal user intervention and parametrization.