Learning to find order in disorder

Humberto Munoz-Bauza; Firas Hamze; Helmut G. Katzgraber

doi:10.1088/1742-5468/ab9e60

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arXiv · 1903.06993

Learning to find order in disorder

Humberto Munoz-Bauza·Firas Hamze·Helmut G. Katzgraber

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Abstract

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.

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BibTeX RIS

Humberto Munoz-Bauza, Firas Hamze, Helmut G. Katzgraber. 2020-07-21. Learning to find order in disorder. https://doi.org/10.1088/1742-5468%2Fab9e60

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