Learning to find order in disorder. (20th July 2020)

• Record Type: Journal Article
• Authors: Munoz-Bauza, Humberto; Hamze, Firas; Katzgraber, Helmut G
• Is Part Of: Journal of statistical mechanics. (2020:Jul.)
• Publication Date: 2020-07-20
• Subjects: machine learning -- spin glasses -- phase transitions -- neural networks; Statistical mechanics -- Periodicals; Mechanics -- Statistical methods -- Periodicals; 530.1305
• DOI: 10.1088/1742-5468/ab9e60
• Languages: English
• ISSNs: 1742-5468

Abstract

Abstract: We introduce the use of neural networks as classifiers on classical disordered systems with no spatial ordering. In this study, we propose a framework of design objectives for learning tasks on disordered systems. Based on our framework, 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. We examine and discuss the use of the neural network in classifying instances from three-dimensional Edwards–Anderson Ising spin-glass in a (random) field.