Machine Learned Phase Transitions in a System of Anisotropic Particles on a Square Lattice

TL;DR

This paper explores machine learning methods, especially CNNs, to classify phases in a system of anisotropic particles on a square lattice, demonstrating high accuracy and the ability to identify critical points without prior estimates.

Contribution

It compares various ML models for phase classification in anisotropic particle systems and introduces physics-guided features to improve accuracy and detect critical points without prior knowledge.

Findings

CNN achieves highest accuracy with snapshot inputs

System size influences model performance

Physics-guided features can outperform CNN in classification

Abstract

The area of Machine learning (ML) has seen exceptional growth in recent years. Successful implementation of ML methods in various branches of physics has led to new insights. These methods have been shown to classify phases in condensed matter systems. Here we study the classification problem of phases in a system of hard rigid rods on a square lattice around a continuous and a discontinuous phase transition. On comparing a number of methods we find that convolutional neural network (CNN) classifies the phases with the highest accuracy when only snapshots are given as inputs. We study how the system size affects the model performance. We further compare the performance of CNN in classifying the phases around a continuous and a discontinuous phase transition. Further, we show that one can even beat the accuracy of CNN with simpler models by using physics-guided features. Lastly, we show that the critical point in this system can be learned without any prior estimate by using only the information of the ordered phase (as training set). Our study reveals the ML techniques that have been successful in studying spin systems can be easily adapted to more complex systems.