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arXiv 2106.11468cond-mat.mes-hallquant-ph
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Floquet拓扑相界的无监督识别

Unsupervised identification of Floquet topological phase boundaries

Nannan Ma, Jiangbin Gong

摘要:

非平衡态拓扑物质一直是一个富有成果的理论和实验研究课题。在非平衡状态下,可能会出现静态系统中不可用的各种奇异拓扑相,这常常挑战我们的物理直觉。如何定位不同非平衡拓扑相之间的边界是便于拓扑表征和进一步理解相变行为的重要问题。在这项工作中,我们开发了一种无监督的机器学习协议,通过将系统在一个驱动周期内的动态,时间维度上的绝热变形以及系统的对称性都纳入我们的机器学习算法,来区分不同的Floquet(周期性驱动)拓扑相位。两个丰富的案例研究结果表明,机器学习能够可靠地揭示复杂的拓扑相边界,因此可以成为一个强大的工具,发现新的拓扑物质所提供的时间维。

英文摘要:

Nonequilibrium topological matter has been a fruitful topic of both theoretical and experimental interest. A great variety of exotic topological phases unavailable in static systems may emerge under nonequilibrium situations, often challenging our physical intuitions. How to locate the borders between different nonequilibrium topological phases is an important issue to facilitate topological characterization and further understand phase transition behaviors. In this work, we develop an unsupervised machine-learning protocol to distinguish between different Floquet (periodically driven) topological phases, by incorporating the system's dynamics within one driving period, adiabatic deformation in the time dimension, plus the system's symmetry all into our machine learning algorithm. Results from two rich case studies indicate that machine learning is able to reliably reveal intricate topological phase boundaries and can hence be a powerful tool to discover novel topological matter afforded by the time dimension.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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