Machine learning phases of matter

Abstract

Neural networks can be used to identify phases and phase transitions in condensed matter systems via supervised machine learning. Readily programmable through modern software libraries, we show that a standard feed-forward neural network can be trained to detect multiple types of order parameter directly from raw state configurations sampled with Monte Carlo. In addition, they can detect highly non-trivial states such as Coulomb phases, and if modified to a convolutional neural network, topological phases with no conventional order parameter. We show that this classification occurs within the neural network without knowledge of the Hamiltonian or even the general locality of interactions. These results demonstrate the power of machine learning as a basic research tool in the field of condensed matter and statistical physics.

18 pages, 8 figures, 1 table

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• Probing Criticality in Quantum Spin Chains with Neural Networks
• Unveiling quantum phase transitions from traps in variational quantum algorithms
• Variational Tensor Neural Networks for Deep Learning
• Machine learning phases of active matter
• Machine learning as an improved estimator for magnetization curve and spin gap
• Spatial mapping of disordered 2D materials: the conductance Sudoku
• Unsupervised Machine Learning of Quenched Gauge Symmetries: A Proof-of-Concept Demonstration
• Machine Learning the Square-Lattice Ising Model
• Machine learning algorithms based on generalized Gibbs ensembles
• Machine learning method to determine concentrations of structural defects in irradiated materials
• Using Machine Learning to Predict the Evolution of Physics Research
• Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
• Neural network enhanced hybrid quantum many-body dynamical distributions
• Machine learning and behavioral economics for personalized choice architecture
• BKT transitions of the XY and six-state clock models on the various two-dimensional lattices
• Principal component analysis of absorbing state phase transitions
• Casimir effect with machine learning
• Phase determination with and without deep learning
• Machine learning of phases and structures for model systems in physics
• Short sighted deep learning
• Emergence of a finite-size-scaling function in the supervised learning of the Ising phase transition
• Minimization of ion micromotion with artificial neural network
• Machine Learning approach to muon spectroscopy analysis
• A simple framework for contrastive learning phases of matter
• Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows
• Decoupling approximation robustly reconstructs directed dynamical networks
• Computational prediction of RNA tertiary structures using machine learning methods
• Learning by Confusion: The Phase Diagram of the Holstein Model
• Unsupervised interpretable learning of topological indices invariant under permutations of atomic bands
• A learning algorithm with emergent scaling behavior for classifying phase transitions
• Robust Identification of Topological Phase Transition by Self-Supervised Machine Learning Approach
• Deep Learning of Phase Transitions for Quantum Spin Chains from Correlation Aspects
• Parametric Optimization of Violin Top Plates using Machine Learning
• Machine learning phases of an Abelian gauge theory
• Compounding meta-atoms into meta-molecules with hybrid artificial intelligence techniques
• Inferring Markovian quantum master equations of few-body observables in interacting spin chains
• Deep analytics of atomically-resolved images: manifest and latent features
• Efficient and quantum-adaptive machine learning with fermion neural networks
• Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe
• Conservation Laws and Spin System Modeling through Principal Component Analysis
• Atom Cloud Detection Using a Deep Neural Network
• Network-Initialized Monte Carlo Based on Generative Neural Networks
• Benchmarking a boson sampler with Hamming nets
• Influence of anisotropy on the study of critical behavior of spin models by machine learning methods
• Detecting Nematic Order in STM/STS Data with Artificial Intelligence
• Machine learning the dynamics of quantum kicked rotor
• Automatic Learning of Topological Phase Boundaries
• Specialising Neural-network Quantum States for the Bose Hubbard Model
• $\beta$-Variational Autoencoder as an Entanglement Classifier
• Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction
• Classification of magnetic order from electronic structure by using machine learning
• Fast reconstruction of single-shot wide-angle diffraction images through deep learning
• Observation of non-Fermi liquid physics in a quantum critical metal via quantum loop topography
• Characterization of a driven two-level quantum system by Supervised Learning
• Neural-network Quantum State of Transverse-field Ising Model
• Deep Learning Hamiltonians from Disordered Image Data in Quantum Materials
• Exact Quantum Algorithms for Quantum Phase Recognition: Renormalization Group and Error Correction
• Machine learning of mirror skin effects in the presence of disorder
• Ground-state properties via machine learning quantum constraints
• Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
• Characterization of photoexcited states in the half-filled one-dimensional extended Hubbard model assisted by machine learning
• Equivariance and generalization in neural networks
• Rise and Fall of Anderson Localization by Lattice Vibrations: A Time-Dependent Machine Learning Approach
• Neural Network Approach to Scaling Analysis of Critical Phenomena
• Thermodynamics of the Ising model encoded in restricted Boltzmann machines
• Probing phase transitions with correlations in configuration space
• Rapid detection of phase transitions from Monte Carlo samples before equilibrium
• Machine-learning the spectral function of a hole in a quantum antiferromagnet
• Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
• Comprehensive studies on the universality of BKT transitions -- Machine-learning study, Monte Carlo simulation, and Level-spectroscopy method
• Exact block encoding of imaginary time evolution with universal quantum neural networks
• Predicting magnetic edge behaviour in graphene using neural networks
• Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model
• Deep learning of phase transitions with minimal examples
• Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics
• Human-machine collaboration: ordering mechanism of rank-2 spin liquid on breathing pyrochlore lattice
• Machine learning the 2D percolation model
• Exploring explicit coarse-grained structure in artificial neural networks
• Inferring Hidden Symmetries of Exotic Magnets from Detecting Explicit Order Parameters
• Machine learning classification of two-dimensional vortex configurations
• Application of the Variational Autoencoder to Detect the Critical Points of the Anisotropic Ising Model
• Learning What a Machine Learns in a Many-Body Localization Transition
• Predicting nucleation near the spinodal in the Ising model using machine learning
• Entanglement transitions from restricted Boltzmann machines
• Extracting Off-Diagonal Order from Diagonal Basis Measurements
• Finding Quantum Critical Points with Neural-Network Quantum States
• Quantum neural networks with multi-qubit potentials
• Ab initio machine learning in chemical compound space
• Black holes and the loss landscape in machine learning
• Machine Learning Percolation Model
• Quaternion-based machine learning on topological quantum systems
• Discovering conservation laws from trajectories via machine learning
• Probing transport in quantum many-fermion simulations via quantum loop topography
• Broken-Symmetry Ground States of the Heisenberg model on the Pyrochlore Lattice
• Deep learning of topological phase transitions from entanglement aspects for two-dimensional chiral p-wave superconductors
• Berezinskii-Kosterlitz-Thouless transition from Neural Network Flows
• Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
• A sampling-guided unsupervised learning method to capture percolation in complex networks
• Machine Learning S-Wave Scattering Phase Shifts Bypassing the Radial Schrödinger Equation
• Deep Learning the Forecast of Galactic Cosmic-Ray Spectra
• QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
• Machine Learning Domain Adaptation in Spin Models with Continuous Phase Transitions
• Efficient Characterization of Quantum Evolutions via a Recommender System
• Machine learning the Ising transition: A comparison between discriminative and generative approaches
• Quantum Convolutional Neural Network for Phase Recognition in Two Dimensions
• Learning phases with Quantum Monte Carlo simulation cell
• Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions
• Probing topological properties of 3D lattice dimer model with neural networks
• Building imaginary-time thermal field theory with artificial neural networks
• Scale-invariant representation of machine learning
• Continuously varying critical exponents in an exactly solvable long-range cluster XY mode
• Mimicking complex dislocation dynamics by interaction networks
• Machine learning, quantum chaos, and pseudorandom evolution
• Investigation of bi-particle states in gate-array-controlled quantum-dot systems aided by machine learning techniques
• Learning of error statistics for the detection of quantum phases
• Boundary between noise and information applied to filtering neural network weight matrices
• Sample generation for the spin-fermion model using neural networks
• Preparing Quantum States by Measurement-feedback Control with Bayesian Optimization
• Efficient learning of ground & thermal states within phases of matter
• Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications
• Extrapolation of polaron properties to low phonon frequencies by Bayesian machine learning
• Fluctuation based interpretable analysis scheme for quantum many-body snapshots
• Effective classical correspondence of the Mott transition
• Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning
• Self-Supervised Ensemble Learning: A Universal Method for Phase Transition Classification of Many-Body Systems
• Weighted Quantum Channel Compiling through Proximal Policy Optimization
• Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer
• Supervised learning of an interacting 2D hard-core boson model of a weak topological insulator using correlation functions
• Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving
• Scanning-probe and information-concealing machine learning intermediate hexatic phase and critical scaling of solid-hexatic phase transition in deformable particles
• Classifying topological neural network quantum states via diffusion maps
• Evaluation of the spectrum of a quantum system using machine learning based on incomplete information about the wavefunctions
• Many-body mobility edges in 1D and 2D revealed by convolutional neural networks
• Quantum circuit complexity and unsupervised machine learning of topological order
• Localization of quantum walk with classical randomness: Comparison between manual methods and supervised machine learning
• Learning phase transitions by siamese neural network
• Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network
• Boosting on the shoulders of giants in quantum device calibration
• Decoding the conductance of disordered nanostructures: a quantum inverse problem
• A Group-Equivariant Autoencoder for Identifying Spontaneously Broken Symmetries
• Comments on the minimal training set for CNN: a case study of the frustrated $J_1$-$J_2$ Ising model on the square lattice
• Deep Learning Super-Diffusion in Multiplex Networks
• Quantum $k$-nearest neighbors algorithm
• AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
• Single-shot quantum measurements sketch quantum many-body states
• Exploring percolation phase transition in the three-dimensional Ising model with machine learning
• Locating quantum critical points with shallow quantum circuits
• Randomized-gauge test for machine learning of Ising model order parameter
• Physical meaning of principal component analysis for classical lattice systems with translational invariance
• Manifold formation and crossings of ultracold lattice spinor atoms in the intermediate interaction regime
• Machine-Learning Detection of the Berezinskii-Kosterlitz-Thouless Transitions
• Deep learning of topological phase transitions from entanglement aspects: An unsupervised way
• Entanglement Clustering for ground-stateable quantum many-body states
• Relevant Analytic Spontaneous Magnetization Relation for the Face-Centered-Cubic Ising Lattice
• Effects of dynamical paths on the energy gap and the corrections to free energy in path integrals of mean-field quantum spin systems
• Artificial intelligence for representing and characterizing quantum systems
• Interpretable Phase Detection and Classification with Persistent Homology
• Neural Scaling Laws for Deep Regression
• Model-Independent Quantum Phases Classifier
• On the criticality of the configuration-space statistical geometry
• Detecting the Largest Correlations using the Correlation Density Matrix: a Quantum Monte Carlo Approach
• A universal neural network for learning phases and criticalities
• Machine-learning semi-local density functional theory for many-body lattice models at zero and finite temperature
• Determination of melting temperature of hexagonal ice using Lee-Yang phase transition theory
• Discovering quasiorder parameters in the Potts model: A bridge between machine learning and critical phenomena
• Interpretable machine-learning identification of the crossover from subradiance to superradiance in an atomic array
• Phase probabilities in first-order transitions using machine learning
• Beyond holography: the entropic quantum gravity foundations of image processing
• Resolution and Relevance Trade-offs in Deep Learning
• Topological Learning in Multi-Class Data Sets
• Identification of hydrodynamic instability by convolutional neural networks
• A Microcanonical Inflection Point Analysis via Parametric Curves and its Relation to the Zeros of the Partition Function
• Efficiency of neural-network state representations of one-dimensional quantum spin systems
• Phase transition for parameter learning of Hidden Markov Models
• Exploring exotic configurations with anomalous features using deep learning: Application of classical and quantum-classical hybrid anomaly detection
• Global exploration of phase behavior in frustrated Ising models using unsupervised learning techniques
• Towards neural reinforcement learning for large deviations in nonequilibrium systems with memory
• High-Freedom Inverse Design with Deep Neural Network for Metasurface Filter in the Visible
• Analysis of Kohn-Sham Eigenfunctions Using a Convolutional Neural Network in Simulations of the Metal-insulator Transition in Doped Semiconductors
• Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics
• Reveal flocking phase transition of self-propelled active particles by machine learning regression uncertainty
• Study of topological quantities of lattice QCD with a modified Wasserstein generative adversarial network
• Emergence of global receptive fields capturing multipartite quantum correlations
• Learning topological defects formation with neural networks in a quantum phase transition
• Machine learning that predicts well may not learn the correct physical descriptions of glassy systems
• Unsupervised Learning of Symmetry Protected Topological Phase Transitions
• Snake net and balloon force with a neural network for detecting multiple phases
• Machine learning of the Ising model on a spherical Fibonacci lattice
• The statistical mechanics and machine learning of the $\alpha$-Rényi ensemble
• Multi-Scale Distributed Representation for Deep Learning and its Application to b-Jet Tagging
• A machine learning assessment of the two states model for lipid bilayer phase transitions
• Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning
• Quantum Phase Recognition via Quantum Attention Mechanism
• Topological characterization of dynamic chiral magnetic textures using machine learning
• New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences
• Weakly-supervised learning on Schrodinger equation
• Tensorization of neural networks for improved privacy and interpretability
• Learning complexity gradually in quantum machine learning models
• Machine learning analysis of dimensional reduction conjecture for nonequilibrium Berezinskii-Kosterlitz-Thouless transition in three dimensions
• Uncovering Magnetic Phases with Synthetic Data and Physics-Informed Training
• Berezinskii-Kosterlitz-Thouless phase transitions of the antiferromagnetic Ising model with ferromagnetic next-nearest-neighbor interactions on the kagome lattice
• Deep learning of thermodynamic laws from microscopic dynamics
• Optimized Observable Readout from Single-shot Images of Ultracold Atoms via Machine Learning
• Machine Learning out of equilibrium correlations in the Bose-Hubbard model
• A deep learning approach to the texture optimization problem for friction control in lubricated contacts
• Numerical study of laser micro- and nano-processing of nanocomposite porous materials
• Learning the gravitational force law and other analytic functions
• Critical Scaling of the Quantum Wasserstein Distance
• Physical discovery in representation learning via conditioning on prior knowledge: applications for ferroelectric domain dynamics
• Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
• Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures
• Sampling the Liquid-Gas Critical Point with Boltzmann Generators
• Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
• Deep learning-based quality filtering of mechanically exfoliated 2D crystals
• Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
• A Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography
• Data-driven criterion for the solid-liquid transition of two-dimensional self-propelled colloidal particles far from equilibrium
• Machine learning in physics: The pitfalls of poisoned training sets
• Unsupervised classification of disordered patterns in an oppositely charged colloidal system
• Identifying phase transitions in physical systems with neural networks: a neural architecture search perspective