论文标题
高斯以人体中心的近似值电位
Gaussian approximation potentials for body-centered-cubic transition metals
论文作者
论文摘要
我们使用高斯近似电势框架为元素V，NB，MO，TA和W开发了一组机器学习的元素间电位。电势显示出良好的精度和可传递性，可弹性，热，液体，缺陷和表面特性。所有潜力都具有准确的排斥潜力，使其适用于涉及高能碰撞的辐射损伤模拟。我们详细研究融化和液体性能，并使用电势为所有五个元素提供高达400 GPA的熔融曲线。
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.
