可转让性
材料科学
高斯分布
原子间势
熔化温度
能量(信号处理)
凝聚态物理
统计物理学
热力学
分子物理学
原子物理学
分子动力学
物理
量子力学
计算机科学
复合材料
机器学习
罗伊特
作者
Jesper Byggmästar,K. Nordlund,Flyura Djurabekova
标识
DOI:10.1103/physrevmaterials.4.093802
摘要
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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