论文标题
材料科学的深度潜力
Deep Potentials for Materials Science
论文作者
论文摘要
为了填补基于经验的原子间潜能的准确（和昂贵）的依据计算和有效的原子模拟之间的空白，已经出现并广泛应用了原子相互作用的新的描述。即机器学习潜力（MLP）。最近开发的MLP类型是深势（DP）方法。在这篇综述中，我们提供了计算材料科学中DP方法的介绍。介绍了DP方法的基础理论以及对其发展和使用的分步介绍。我们还回顾了DPS在各种材料系统中的材料应用。 DP库为开发DPS和现存DPS数据库提供了一个平台。我们讨论了与从头算法和经验潜能相比，DPS的准确性和效率。
To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.
