计算机科学
代表(政治)
人工智能
任务(项目管理)
班级(哲学)
领域(数学)
特征(语言学)
开发(拓扑)
相(物质)
机器学习
理论计算机科学
物理
数学
系统工程
数学分析
语言学
哲学
量子力学
政治
政治学
纯数学
法学
工程类
作者
Xiao-Qi Han,Shijie Xu,Zhen Feng,Rong-Qiang He,Zhong-Yi Lu
标识
DOI:10.1088/0256-307x/40/2/027501
摘要
A main task in condensed-matter physics is to recognize, classify, and characterize phases of matter and the corresponding phase transitions, for which machine learning provides a new class of research tools due to the remarkable development in computing power and algorithms. Despite much exploration in this new field, usually different methods and techniques are needed for different scenarios. Here, we present SimCLP: a simple framework for contrastive learning phases of matter, which is inspired by the recent development in contrastive learning of visual representations. We demonstrate the success of this framework on several representative systems, including non-interacting and quantum many-body, conventional and topological. SimCLP is flexible and free of usual burdens such as manual feature engineering and prior knowledge. The only prerequisite is to prepare enough state configurations. Furthermore, it can generate representation vectors and labels and hence help tackle other problems. SimCLP therefore paves an alternative way to the development of a generic tool for identifying unexplored phase transitions.
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