Gaussian approximation potentials for body-centered-cubic transition metals

TL;DR

This paper introduces machine-learning interatomic potentials for BCC transition metals V, Nb, Mo, Ta, and W, accurately modeling various properties and enabling high-pressure melting simulations.

Contribution

The authors develop and validate Gaussian approximation potentials for five BCC transition metals, enhancing accuracy and transferability for diverse physical conditions.

Findings

Potentials accurately predict elastic, thermal, and defect properties.

Potentials are suitable for radiation damage simulations.

Melting curves up to 400 GPa are provided for all five elements.

Abstract

We develop a set of machine-learning interatomic potentials for elemental V, Nb, Mo, Ta, and W using the Gaussian approximation potential framework. The potentials show good accuracy and transferability for elastic, thermal, liquid, defect, and surface properties. All potentials are augmented with accurate repulsive potentials, making them applicable to radiation damage simulations involving high-energy collisions. We study melting and liquid properties in detail and use the potentials to provide melting curves up to 400 GPa for all five elements.