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Machine learning is capable of discriminating phases of matter, and findingassociated phase transitions, directly from large data sets of raw stateconfigurations. In the context of condensed matter physics, most progress inthe field of supervised learning has come from employing neural networks asclassifiers. Although very powerful, such algorithms suffer from a lack ofinterpretability, which is usually desired in scientific applications in orderto associate learned features with physical phenomena. In this paper, weexplore support vector machines (SVMs) which are a class of supervised kernelmethods that provide interpretable decision functions. We find that SVMs canlearn the mathematical form of physical discriminators, such as orderparameters and Hamiltonian constraints, for a set of two-dimensional spinmodels: the ferromagnetic Ising model, a conserved-order-parameter Ising model,and the Ising gauge theory. The ability of SVMs to provide interpretableclassification highlights their potential for automating feature detection inboth synthetic and experimental data sets for condensed matter and othermany-body systems.

Kernel methods for interpretable machine learning of order parameters