从实验可观测物中学习相互作用的拓相的无监督学习
Li-Wei Yu1,2, Shun-Yao Zhang2, Pei-Xin Shen2
1Theoretical Physics Division, Chern Institute of Mathematics and LPMC, Nankai University, Tianjin 300071, China.
Fundamental research
|December 11, 2024
概括
我们开发了一种无监督的机器学习方法,使用实验数据对交互的拓阶段进行分类. 这种基于扩散图和格林函数的方法简化了识别复杂的拓材料.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 量子材料是一种量子材料.
- 机器学习应用 机器学习应用
背景情况:
- 将物质的拓相分类,特别是那些具有强相互作用的物质,是凝聚物质物理学中的一个重大挑战.
- 现有的方法往往需要先验知识或计算上昂贵的哈密尔顿对角化.
研究的目的:
- 提出和验证一种无监督的机器学习方法,用于对称性保护的交互拓相的分类.
- 为了能够直接从实验观测值进行分类,而无需对系统的哈密尔顿数的先前了解.
主要方法:
- 利用来自实验可测量的光谱函数的Green函数,作为基于扩散图的机器学习模型的输入数据.
- 通过广泛的数值模拟,通过一维交互的拓绝缘体模型演示方法.
- 通过动量分辨率拉曼光谱法,提出一种用于测量超冷原子系统中的光谱函数的通用方案.
主要成果:
- 扩散地图方法成功地将研究模型中的交互的拓相分类.
- 该方法有效地使用光谱函数作为输入,绕过了哈密尔顿对角化的需要.
- 介绍了一种用于测量超冷原子中的光谱函数的实用实验协议.
结论:
- 拟议的无监督机器学习方法提供了一种多功能和自主协议,用于识别交互的拓阶段.
- 这种方法显著简化了从实验数据中对复杂的拓材料的表征.
- 这项工作为机器学习在拓阶段发现中的更广泛应用铺平了道路.
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