Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC

TL;DR

This paper introduces a Bayesian active learning workflow for training interatomic force fields efficiently, enabling accurate modeling of phase transformations and thermal properties in SiC with significantly reduced computational cost.

Contribution

The work presents a novel Bayesian active learning method using Gaussian process regression for force field development, achieving rapid training and high accuracy in simulating SiC.

Findings

Successfully trained a SiC force field in days

Accurately captured pressure-induced phase transformations

Outperformed existing models in vibrational and thermal properties

Abstract

Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.