Bedolla Edwin, Padierna Luis Carlos, Castañeda-Priego Ramón
División de Ciencias e Ingenierías, Universidad de Guanajuato, Loma del Bosque 103, 37150 León, Mexico.
J Phys Condens Matter. 2020 Nov 5;33(5). doi: 10.1088/1361-648X/abb895.
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.
凝聚态物理（CMP）旨在理解物质在量子和原子层面的微观相互作用，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相互重叠，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，将这些算法应用于推进CMP研究的浓厚兴趣在这两个领域的交叉点上创造了一个引人注目的新研究领域。在本综述中，我们旨在探索CMP中的主要领域，这些领域已成功应用ML技术来推进研究，例如用于势能面的ML方案的描述和使用、晶格系统中物质拓扑相的表征、非晶格和原子模拟中相变的预测、用物理启发框架解释ML理论以及用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展望。
