机器学习在凝聚态物理中的应用
Machine Learning for Condensed Matter Physics
摘要：
凝聚态物理（CMP）旨在理解物质在量子和原子水平上的微观相互作用，并描述这些相互作用如何导致介观和宏观性质。CMP与许多其他重要的科学分支重叠，如化学，材料科学，统计物理和高性能计算。随着现代机器学习（ML）技术的进步，人们对将这些算法应用于进一步的CMP研究产生了浓厚的兴趣，这在两个领域的交叉点上创造了一个引人注目的新研究领域。在这篇综述中，我们的目标是探索CMP中的主要领域，这些领域已经成功地将ML技术应用于进一步的研究，例如势能面的ML方案的描述和使用，晶格系统中物质的拓扑相的表征，非晶格和原子模拟中相变的预测，用物理框架解释ML理论，用ML算法增强模拟方法。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，以及未来发展的一些前景。
英文摘要：
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.
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