论文标题
凝结物理的机器学习
Machine Learning for Condensed Matter Physics
论文作者
论文摘要
凝结物理学（CMP）试图了解量子和原子水平上物质的微观相互作用，并描述了这些相互作用如何导致介观和宏观特性。 CMP与许多其他重要科学分支重叠，例如化学，材料科学，统计物理学和高性能计算。随着现代机器学习（ML）技术的进步，对将这些算法应用于进一步的CMP研究的浓厚兴趣在这两个领域的交集中创造了引人注目的新研究领域。 In this review, we aim to explore the main areas within CMP, which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, the interpretation of ML theories with physics-inspired frameworks and the enhancement of simulation methods with ML算法。我们还详细讨论了在CMP问题上使用ML方法的主要挑战和缺点，以及对未来发展的一些观点。
Condensed Matter Physics (CMP) seeks to understand the microscopic interactions of matter at the quantum and atomistic levels, and describes how these interactions result in both mesoscopic and macroscopic properties. CMP overlaps with many other important branches of science, such as Chemistry, Materials Science, Statistical Physics, and High-Performance Computing. With the advancements in modern Machine Learning (ML) technology, a keen interest in applying these algorithms to further CMP research has created a compelling new area of research at the intersection of both fields. In this review, we aim to explore the main areas within CMP, which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, the interpretation of ML theories with physics-inspired frameworks and the enhancement of simulation methods with ML algorithms. We also discuss in detail the main challenges and drawbacks of using ML methods on CMP problems, as well as some perspectives for future developments.
