# Learning to find order in disorder

## TL;DR

This paper demonstrates that neural networks can effectively classify disordered systems, specifically identifying spin-glass states in the 3D Edwards-Anderson model, and helps clarify phase boundaries in such complex systems.

## Contribution

The study introduces a neural network approach for classifying disordered systems and applies it to the 3D Edwards-Anderson model to determine phase boundaries.

## Key findings

- Neural network accurately identifies spin-glass states.
- Classifies phase boundaries consistent with theoretical expectations.
- Supports absence of Almeida-Thouless line in the model.

## 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.