Machine-Learning Studies on Spin Models

Abstract

With the recent developments in machine learning, Carrasquilla and Melko have proposed a paradigm that is complementary to the conventional approach for the study of spin models. As an alternative to investigating the thermal average of macroscopic physical quantities, they have used the spin configurations for the classification of the disordered and ordered phases of a phase transition through machine learning. We extend and generalize this method. We focus on the configuration of the long-range correlation function instead of the spin configuration itself, which enables us to provide the same treatment to multi-component systems and the systems with a vector order parameter. We analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition with the same technique to classify three phases: the disordered, the BKT, and the ordered phases. We also present the classification of a model using the training data of a different model.

accepted for publication in Scientific Reports; main text + supplementary information

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Cited by in corpus (37)

• Machine Learning for Condensed Matter Physics
• Mapping distinct phase transitions to a neural network
• Exploring neural network training strategies to determine phase transitions in frustrated magnetic models
• Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transitions in the q-state clock models
• Finite-size analysis in neural network classification of critical phenomena
• Inverse Renormalization Group based on Image Super-Resolution using Deep Convolutional Networks
• Neural Network flows of low q-state Potts and clock Models
• Machine-Learning Study using Improved Correlation Configuration and Application to Quantum Monte Carlo Simulation
• A learning by confusion approach to characterize phase transitions
• Unsupervised machine learning approaches to the $q$-state Potts model
• Machine-learning detection of the Berezinskii-Kosterlitz-Thouless transition and the second-order phase transition in the XXZ models
• Machine learning generated configurations in presence of a conserved quantity: a cautionary tale
• Machine learning for percolation utilizing auxiliary Ising variables
• Can a CNN trained on the Ising model detect the phase transition of the $q$-state Potts model?
• Engineering Topological Phases Guided by Statistical and Machine Learning Methods
• Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks
• Phase determination with and without deep learning
• Machine learning of phases and structures for model systems in physics
• BKT transitions of the XY and six-state clock models on the various two-dimensional lattices
• Emergence of a finite-size-scaling function in the supervised learning of the Ising phase transition
• Minimalist Neural Networks training for phase classification in diluted-Ising models
• Automatic Learning of Topological Phase Boundaries
• Classification of magnetic order from electronic structure by using machine learning
• Comprehensive studies on the universality of BKT transitions -- Machine-learning study, Monte Carlo simulation, and Level-spectroscopy method
• Probing phase transitions with correlations in configuration space
• Application of the Variational Autoencoder to Detect the Critical Points of the Anisotropic Ising Model
• Berezinskii-Kosterlitz-Thouless transition from Neural Network Flows
• Machine learning classification of two-dimensional vortex configurations
• Machine Learning Domain Adaptation in Spin Models with Continuous Phase Transitions
• Self-Supervised Ensemble Learning: A Universal Method for Phase Transition Classification of Many-Body Systems
• Learning phases with Quantum Monte Carlo simulation cell
• Supervised learning of an interacting 2D hard-core boson model of a weak topological insulator using correlation functions
• Machine-Learning Detection of the Berezinskii-Kosterlitz-Thouless Transitions
• Model-Independent Quantum Phases Classifier
• Discovering quasiorder parameters in the Potts model: A bridge between machine learning and critical phenomena
• A Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography
• Berezinskii-Kosterlitz-Thouless phase transitions of the antiferromagnetic Ising model with ferromagnetic next-nearest-neighbor interactions on the kagome lattice