Gaussian approximation potentials for body-centered-cubic transition metals

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.

References in corpus (7)

• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• On-the-fly machine learning force field generation: Application to melting points
• Force-matched embedded-atom method potential for niobium
• Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
• Ab initio melting curve of molybdenum by the phase coexistence method
• Data-driven Material Models for Atomistic Simulation