论文标题
金属和合金系统的多项式机器学习潜力的系统开发
Systematic development of polynomial machine learning potentials for metallic and alloy systems
论文作者
论文摘要
从密度功能理论（DFT）计算构建的广泛数据集开发的机器学习潜力（MLP）对许多研究人员变得越来越有吸引力。本文提出了一个基于多项式的MLP的框架，称为多项式MLP。还展示了许多元素和二元合金系统的准确和计算有效多项式MLP的系统开发及其针对各种属性的预测能力。因此，许多多项式MLP都可以在存储库网站上找到。存储库将帮助许多科学家进行准确有效的大规模原子模拟和晶体结构搜索。
Machine learning potentials (MLPs) developed from extensive datasets constructed from density functional theory (DFT) calculations have become increasingly appealing for many researchers. This paper presents a framework of polynomial-based MLPs, called polynomial MLPs. The systematic development of accurate and computationally efficient polynomial MLPs for many elemental and binary alloy systems and their predictive powers for various properties are also demonstrated. Consequently, many polynomial MLPs are available in a repository website. The repository will help many scientists perform accurate and efficient large-scale atomistic simulations and crystal structure searches.
