Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters

TL;DR

This paper introduces a machine learning optimized classical potential for molecular dynamics that accurately models borosilicate glass structures across various compositions, validated against experimental data.

Contribution

The work presents a transferable, ML-optimized potential that captures structural variations in borosilicate glasses, including validation against experimental X-ray data, with analysis of force field parameters' effects.

Findings

Accurately predicts short- and medium-range order in borosilicate glasses.

Validates against experimental X-ray structure factor data.

Provides insights into how force field parameters influence glass properties.

Abstract

The simulation of borosilicate glasses is challenging due to the composition and temperature dependent coordination state of boron atoms. Here, we present a newly developed machine learning optimized classical potential for molecular dynamics simulations that achieves transferability across diverse borosilicate glass compositions. Our potential accurately predicts the glass structural variations in short- and medium-range order in different glass compositions, including validating our potential against experimental X-ray structure factor data. Notably, these data are not included in the optimization framework, which focuses exclusively on density and four-fold coordinated boron fraction. We further investigate the impact of empirical parameters in the force field formulation on the microscopic bond lengths, bond angles and the macroscopic densities, providing new insights into the relationship between interatomic potentials and bulk glass behaviors.