Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics

TL;DR

This paper introduces a deep learning-based method called TDDPMD that enables fast and accurate simulation of warm dense matter across a wide range of temperatures and densities, surpassing traditional FPMD in efficiency and reliability.

Contribution

The authors develop TDDPMD, a novel electron temperature dependent deep potential molecular dynamics scheme that significantly improves simulation speed and accuracy for warm dense matter.

Findings

TDDPMD is several orders of magnitude faster than FPMD.

TDDPMD accurately reproduces structural properties of beryllium.

TDDPMD provides more reliable diffusion coefficients than FPMD.

Abstract

Simulating warm dense matter that undergoes a wide range of temperatures and densities is challenging. Predictive theoretical models, such as quantum-mechanics-based first-principles molecular dynamics (FPMD), require a huge amount of computational resources. Herein, we propose a deep learning based scheme, called electron temperature dependent deep potential molecular dynamics (TDDPMD), for efficiently simulating warm dense matter with the accuracy of FPMD. The TDDPMD simulation is several orders of magnitudes faster than FPMD, and, unlike FPMD, its efficiency is not affected by the electron temperature. We apply the TDDPMD scheme to beryllium (Be) in a wide range of temperatures (0.4 to 2500 eV) and densities (3.50 to 8.25 g/cm$^3$). Our results demonstrate that the TDDPMD method not only accurately reproduces the structural properties of Be along the principal Hugoniot curve at the FPMD level, but also yields even more reliable diffusion coefficients than typical FPMD simulations due to its ability to simulate larger systems with longer time.