Identifying phase transitions in physical systems with neural networks: a neural architecture search perspective

TL;DR

This paper explores how neural architecture search can optimize neural networks for detecting phase transitions in physical systems, using neuron coverage metrics to assess and improve the detection capability.

Contribution

It introduces a novel approach to optimize neural network architectures for phase transition detection and evaluates neuron coverage metrics as tools for this purpose.

Findings

Neuron coverage metrics are promising for phase transition detection

Optimized neural architectures improve phase analysis accuracy

Neural architecture search enhances understanding of physical system states

Abstract

The use of machine learning algorithms to investigate phase transitions in physical systems is a valuable way to better understand the characteristics of these systems. Neural networks have been used to extract information of phases and phase transitions directly from many-body configurations. However, one limitation of neural networks is that they require the definition of the model architecture and parameters previous to their application, and such determination is itself a difficult problem. In this paper, we investigate for the first time the relationship between the accuracy of neural networks for information of phases and the network configuration (that comprises the architecture and hyperparameters). We formulate the phase analysis as a regression task, address the question of generating data that reflects the different states of the physical system, and evaluate the performance of neural architecture search for this task. After obtaining the optimized architectures, we further implement smart data processing and analytics by means of neuron coverage metrics, assessing the capability of these metrics to estimate phase transitions. Our results identify the neuron coverage metric as promising for detecting phase transitions in physical systems.