Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica

TL;DR

This paper extends the self-learning hybrid Monte Carlo method to isothermal-isobaric ensembles, enabling efficient first-principles simulations of liquids like silica near melting points, with results matching experimental data.

Contribution

The authors develop an extended SLHMC method for isothermal-isobaric ensembles, broadening its application to soft materials and liquids with large volume fluctuations.

Findings

Accurate static structure factor for liquid silica near melting point.

Good agreement between first-principles simulations and high-energy X-ray data.

Enhanced sampling efficiency in simulating complex liquids.

Abstract

Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory. The statistical sampling can be accelerated with the assistance of smart trial moves by machine learning potentials. In the first report (Nagai, {\it et al}. Phys. Rev. B 102, 041124(R) (2020)), the SLHMC approach was introduced for the simplest case of canonical sampling. We herein extend this idea to isothermal-isobaric ensembles to enable general applications for soft materials and liquids with large volume fluctuation. As a demonstration, the isothermal-isobaric SLHMC method was used to study the vibrational structure of liquid silica at temperatures close to the melting point, whereby the slow diffusive motion is beyond the time scale of first-principles molecular dynamics. It was found that the static structure factor thus computed from first-principles agrees quite well with the high-energy X-ray data.