Phase diagram study of a two-dimensional frustrated antiferromagnet via unsupervised machine learning

We apply unsupervised learning techniques to classify the different phases of the J1−J2 antiferromagnetic Ising model on the honeycomb lattice. We construct the phase diagram of the system using convolutional autoencoders. These neural networks can detect phase transitions in the system via "an...

Full description

Saved in:
Bibliographic Details
Published in:Physical review. B 2021-04, Vol.103 (13), Article 134422
Main Authors: Acevedo, S., Arlego, M., Lamas, C. A.
Format: Article
Language:English
Subjects:
Anomalies
Antiferromagnetism
High temperature
Honeycomb construction
Ising model
Machine learning
Neural networks
Phase diagrams
Phase transitions
Phases
Training
Unsupervised learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We apply unsupervised learning techniques to classify the different phases of the J1−J2 antiferromagnetic Ising model on the honeycomb lattice. We construct the phase diagram of the system using convolutional autoencoders. These neural networks can detect phase transitions in the system via "anomaly detection" without the need for any label or a priori knowledge of the phases. We present different ways of training these autoencoders, and we evaluate them to discriminate between distinct magnetic phases. In this process, we highlight the case of high-temperature or even random training data. Finally, we analyze the capability of the autoencoder to detect the ground state degeneracy through the reconstruction error.
ISSN:2469-9950
2469-9969
DOI:10.1103/PhysRevB.103.134422