期刊
PHYSICAL REVIEW B
卷 96, 期 20, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.96.205146
关键词
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资金
- NSERC
- Canada Research Chair program from Fundacao para a Ciencia e a Tecnologia (Portugal) [SFRH/BD/84875/2012]
- Perimeter Institute for Theoretical Physics
- Industry Canada
- Province of Ontario through the Ministry of Research Innovation
- Fundação para a Ciência e a Tecnologia [SFRH/BD/84875/2012] Funding Source: FCT
Machine learning is capable of discriminating phases ofmatter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines (SVMs), which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.
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