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.

中文翻译：

---

体心立方过渡金属的高斯近似势

我们使用高斯近似势能框架为元素V，Nb，Mo，Ta和W开发了一套机器学习的原子间势。这些电势对于弹性，热，液体，缺陷和表面特性显示出良好的精度和可传递性。所有电势均增加了精确的排斥电势，使其适用于涉及高能碰撞的辐射损伤模拟。我们将详细研究熔融和液体特性，并利用电势为所有五个元素提供高达400 GPa的熔融曲线。