| CONDENSED MATTER: ELECTRONIC STRUCTURE, ELECTRICAL, MAGNETIC, AND OPTICAL PROPERTIES |
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Framework for Contrastive Learning Phases of Matter Based on Visual Representations |
| Xiao-Qi Han, Sheng-Song Xu, Zhen Feng, Rong-Qiang He*, and Zhong-Yi Lu* |
| Department of Physics, Renmin University of China, Beijing 100872, China |
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| Cite this article: |
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Xiao-Qi Han, Sheng-Song Xu, Zhen Feng et al 2023 Chin. Phys. Lett. 40 027501 |
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Abstract 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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Received: 07 November 2022
Published: 17 January 2023
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| PACS: |
05.70.Fh
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(Phase transitions: general studies)
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75.40.Mg
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(Numerical simulation studies)
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02.70.Uu
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(Applications of Monte Carlo methods)
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64.70.Tg
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(Quantum phase transitions)
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