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.

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• Machine learned Force-Fields for an ab-initio Quality Description of Metal-Organic Frameworks
• BIGDML: Towards Exact Machine Learning Force Fields for Materials
• Simple machine-learned interatomic potentials for complex alloys
• A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment
• Full-scale ab initio simulations of laser-driven atomistic dynamics
• Efficient atomistic simulations of radiation damage in W and W-Mo using machine-learning potentials
• A "Magnetic" Machine Learning Interatomic Potential for Nickel
• Classical and Machine Learning Interatomic Potentials for BCC Vanadium
• Modeling Extensive Defects in Metals through Classical Potential-Guided Sampling and Automated Configuration Reconstruction
• Computational study of crystal defects formation in Mo by machine learned molecular dynamics simulations
• Segregation, ordering, and precipitation in WTaV-based concentrated refractory alloys
• Temperature effects on the point defects formation in [111] W by neutron induced collision cascade
• Efficient moment tensor machine-learning interatomic potential for accurate description of defects in Ni-Al Alloys
• Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: defects and segregation
• High-pressure melting and elastic behavior of vanadium and niobium based on ab initio and machine learning molecular dynamics