The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science setting so far. Here we introduce an unsupervised machine-learning scheme for detecting phase transitions with a pair of discriminative cooperative networks (DCN). In this scheme, a guesser network and a learner network cooperate to detect phase transitions from fully unlabeled data. The new scheme is efficient enough for dealing with phase diagrams in two-dimensional parameter spaces, where we can utilize an active contour model -- the snake -- from computer vision to host the two networks. The snake, with a DCN "brain", moves and learns actively in the parameter space, and locates phase boundaries automatically.
@article{arxiv.1706.08111,
title = {Discriminative Cooperative Networks for Detecting Phase Transitions},
author = {Ye-Hua Liu and Evert P. L. van Nieuwenburg},
journal= {arXiv preprint arXiv:1706.08111},
year = {2018}
}