We introduce the use of neural networks as classifiers on classical disordered systems with no spatial ordering. In this study, we implement a convolutional neural network trained to identify the spin-glass state in the three-dimensional Edwards-Anderson Ising spin-glass model from an input of Monte Carlo sampled configurations at a given temperature. The neural network is designed to be flexible with the input size and can accurately perform inference over a small sample of the instances in the test set. Using the neural network to classify instances of the three-dimensional Edwards-Anderson Ising spin-glass in a (random) field we show that the inferred phase boundary is consistent with the absence of an Almeida-Thouless line.
@article{arxiv.1903.06993,
title = {Learning to find order in disorder},
author = {Humberto Munoz-Bauza and Firas Hamze and Helmut G. Katzgraber},
journal= {arXiv preprint arXiv:1903.06993},
year = {2020}
}