Ab initio quality neural-network potential for sodium

TL;DR

This paper develops a neural-network-based interatomic potential for sodium that accurately models its properties under high-pressure and high-temperature conditions, enabling high-quality molecular dynamics simulations.

Contribution

A novel neural-network potential for sodium that reproduces ab initio accuracy across multiple phases and conditions, improving simulation capabilities.

Findings

Accurately models liquid and crystalline sodium properties up to 120 GPa and 1200 K.

Reproduces experimental data quantitatively across a wide P-T range.

Enables high-fidelity molecular dynamics simulations of HPHT sodium.

Abstract

An interatomic potential for high-pressure high-temperature (HPHT) crystalline and liquid phases of sodium is created using a neural-network (NN) representation of the ab initio potential energy surface. It is demonstrated that the NN potential provides an ab initio quality description of multiple properties of liquid sodium and bcc, fcc, cI16 crystal phases in the P-T region up to 120 GPa and 1200 K. The unique combination of computational efficiency of the NN potential and its ability to reproduce quantitatively experimental properties of sodium in the wide P-T range enables molecular dynamics simulations of physicochemical processes in HPHT sodium of unprecedented quality.