Kernel methods for interpretable machine learning of order parameters

Understand

Machine learning is capable of discriminating phases of matter, 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.