Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system

TL;DR

This paper explores the use of supervised and unsupervised machine learning techniques to accurately identify phase boundaries in a nonequilibrium active Ising model, emphasizing interpretability and generalization beyond training data.

Contribution

It demonstrates that unsupervised learning effectively detects phase boundaries, and supervised neural networks can generalize phase diagram knowledge with interpretability methods.

Findings

Unsupervised learning identifies phase boundaries even during phase coexistence.

Supervised neural networks predict phase boundaries beyond trained control variables.

Interpretability methods enable physically meaningful phase classification.

Abstract

Still under debate is the question of whether machine learning is capable of going beyond black-box modeling for complex physical systems. We investigate the generalizing and interpretability properties of learning algorithms. To this end, we use supervised and unsupervised learning to infer the phase boundaries of the active Ising model, starting from an ensemble of configurations of the system. We illustrate that unsupervised learning techniques are powerful at identifying the phase boundaries in the control parameter space, even in situations of phase coexistence. It is demonstrated that supervised learning with neural networks is capable of learning the characteristics of the phase diagram, such that the knowledge obtained at a limited set of control variables can be used to determine the phase boundaries across the phase diagram. In this way, we show that properly designed supervised learning provides predictive power to regions in the phase diagram that are not included in the training phase of the algorithm. We stress the importance of introducing interpretability methods in order to perform a physically relevant classification of the phases with deep learning.