论文标题
复杂合金的简单机器学习的原子间潜力
Simple machine-learned interatomic potentials for complex alloys
论文作者
论文摘要
由于训练数据必须对包含许多元素的材料，开发包含许多元素的材料的机器学习间潜力变得越来越具有挑战性。我们研究了具有不同元素合金的机器学习率的学习率和可实现的准确性，这些元素合金具有不同的描述符组合的本地原子环境。我们表明，对于五元素合金系统，使用简单的低维描述符的电势可以通过适度尺寸的训练数据集达到MEV/ATOM - 精度，在数据效率，准确性和速度方面的高度肥皂描述符显着优于高维肥皂描述符。特别是，我们为Mo-NB-TA-V-W合金开发了计算快速的机器学习和表格的高斯近似电势（TABGAP），其组合是基于嵌入式原子方法的两体，三体和新的简单标量多体密度描述符。
Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning rates and achievable accuracy of machine-learning interatomic potentials for many-element alloys with different combinations of descriptors for the local atomic environments. We show that for a five-element alloy system, potentials using simple low-dimensional descriptors can reach meV/atom-accuracy with modestly sized training datasets, significantly outperforming the high-dimensional SOAP descriptor in data efficiency, accuracy, and speed. In particular, we develop a computationally fast machine-learned and tabulated Gaussian approximation potential (tabGAP) for Mo-Nb-Ta-V-W alloys with a combination of two-body, three-body, and a new simple scalar many-body density descriptor based on the embedded atom method.
