Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems
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DOI：
10.7566/jpsj.85.123706
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
T. Ohtsuki;T. Ohtsuki
中科院分区：
文献类型：
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作者：
T. Ohtsuki;T. Ohtsuki
随机电子系统表现出丰富的相，如安德森绝缘体、扩散金属、量子和反常量子霍尔绝缘体、Weyl半金属以及强/弱拓扑绝缘体。每个物质相的本征函数都有其特定的特征，但由于系统的随机性，从本征函数判断物质相比较困难。在这里，我们提出了深度学习算法来捕捉特征函数的特征。讨论了局域-非局域跃迁和无序陈氏绝缘子-安德森绝缘子跃迁。
Random electron systems show rich phases such as Anderson insulator, diffusive metal, quantum and anomalous quantum Hall insulator, Weyl semimetal, as well as strong/weak topological insulators. Eigenfunctions of each matter phase have specific features, but due to the random nature of systems, judging the matter phase from eigenfunctions is difficult. Here we propose the deep learning algorithm to capture the features of eigenfunctions. Localization-delocalization transition as well as disordered Chern insulator-Anderson insulator transition is discussed.