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

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.

To be published in the Journal of Chemical Physics

References in corpus (3)

• Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
• Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
• Superfragile glassy dynamics of onecomponent system with isotropic potential: competition of diffusion and frustration

Cited by in corpus (4)

• Atomic diffusion due to hyperatomic fluctuation for quasicrystals
• Topological data analysis for revealing structural origin of density anomalies in silica glass
• Self-learning Monte Carlo with equivariant Transformer
• Self-learning path integral hybrid Monte Carlo with mixed ab initio and machine learning potentials for modeling nuclear quantum effects in water