Machine Learning in Electronic Quantum Matter Imaging Experiments

Abstract

Essentials of the scientific discovery process have remained largely unchanged for centuries: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into established scientific theory. Today, however, we face major challenges because automated instrumentation and large-scale data acquisition are generating data sets of such volume and complexity as to defy human analysis. Radically different scientific approaches are needed, with machine learning (ML) showing great promise, not least for materials science research. Hence, given recent advances in ML analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is for ML to engage equivalently with experimental data. For example, atomic-scale visualization of EQM yields arrays of complex electronic structure images, that frequently elude effective analyses. Here we report development and training of an array of artificial neural networks (ANN) designed to recognize different types of hypothesized order hidden in EQM image-arrays. These ANNs are used to analyze an experimentally-derived EQM image archive from carrier-doped cuprate Mott insulators. Throughout these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs find these phenomena to be unidirectional, revealing a coincident nematic EQM state. Strong-coupling theories of electronic liquid crystals are congruent with all these observations.

arXiv comment44 pages, 15 figures

References in corpus (19)

• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Machine learning phases of matter
• Electronic Liquid Crystal Phases of a Doped Mott Insulator
• Theory of Intertwined Orders in High Temperature Superconductors
• Many-body quantum state tomography with neural networks
• Learning phase transitions by confusion
• Nematic Fermi Fluids in Condensed Matter Physics
• Machine learning modeling of superconducting critical temperature
• Resonant x-ray scattering studies of charge order in cuprates
• Machine learning quantum phases of matter beyond the fermion sign problem
• Quantum Loop Topography for Machine Learning
• Quenched disorder and vestigial nematicity in the pseudo-gap regime of the cuprates
• A Neural Decoder for Topological Codes
• Probing many-body localization with neural networks
• Atomic-scale Electronic Structure of the Cuprate d-Symmetry Form Factor Density Wave State
• Distinguishing Patterns of Charge Order: Stripes or Checkerboards
• Discovering the Building Blocks of Atomic Systems using Machine Learning
• Commensurate $4a_0$ period Charge Density Modulations throughout the $B{i}_{2}S{r}_{2}CaC{u}_2{O}_{8+x}$ Pseudogap Regime
• From stripe to checkerboard order on the square lattice in the presence of quenched disorder

Cited by in corpus (87)

• Machine learning and the physical sciences
• The Physics of Pair Density Waves
• Study of the Two-Dimensional Frustrated J1-J2 Model with Neural Network Quantum States
• Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data
• Towards high-throughput superconductor discovery via machine learning
• The 2021 Quantum Materials Roadmap
• Quantum Adversarial Machine Learning
• Classifying Snapshots of the Doped Hubbard Model with Machine Learning
• Topological Quantum Compiling with Reinforcement Learning
• Discovering Quantum Phase Transitions with Fermionic Neural Networks
• Unsupervised machine learning of topological phase transitions from experimental data
• Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak
• Precise measurement of quantum observables with neural-network estimators
• Unsupervised machine learning of quantum phase transitions using diffusion maps
• Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
• Markovian Quantum Neuroevolution for Machine Learning
• Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction
• Unsupervised learning universal critical behavior via the intrinsic dimension
• Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
• Interpreting machine learning of topological quantum phase transitions
• One-Component Order Parameter in URu${}_2$Si${}_2$ Uncovered by Resonant Ultrasound Spectroscopy and Machine Learning
• CRYSPNet: Crystal Structure Predictions via Neural Network
• Atomic-scale Electronic Structure of the Cuprate Pair Density Wave State Coexisting with Superconductivity
• Locally commensurate charge-density wave with three-unit-cell periodicity in YBCO
• Visualizing Strange Metallic Correlations in the 2D Fermi-Hubbard Model with AI
• Accelerating lattice quantum Monte Carlo simulation using artificial neural networks: an application to the Holstein model
• Neural network setups for a precise detection of the many-body localization transition: finite-size scaling and limitations
• Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials
• Phase diagram study of a two-dimensional frustrated antiferromagnet via unsupervised machine learning
• Analyzing non-equilibrium quantum states through snapshots with artificial neural networks
• Universal Adversarial Examples and Perturbations for Quantum Classifiers
• Machine learning spectral indicators of topology
• Revealing quantum chaos with machine learning
• Linking the pseudo-gap in the cuprates with local symmetry breaking: a commentary
• Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin Liquids
• A perspective on machine learning and data science for strongly correlated electron problems
• Atomic-level Characterisation of Quantum Computer Arrays by Machine Learning
• Cataloging Accreted Stars within Gaia DR2 using Deep Learning
• Embedding Human Heuristics in Machine-Learning-Enabled Probe Microscopy
• Deep learning analysis of polaritonic waves images
• Machine-learning assisted quantum control in random environment
• Charting the low-loss region in Electron Energy Loss Spectroscopy with machine learning
• Active Learning Algorithm for Computational Physics
• Certification of quantum states with hidden structure of their bitstrings
• Machine-Learned Phase Diagrams of Generalized Kitaev Honeycomb Magnets
• Machine Learning Microscopic Form of Nematic Order in twisted double-bilayer graphene
• Machine learning effective models for quantum systems
• Machine learning aided atomic structure identification of interfacial ionic hydrates from atomic force microscopy images
• Mapping out phase diagrams with generative classifiers
• Replacing neural networks by optimal analytical predictors for the detection of phase transitions
• Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning
• Experimental demonstration of adversarial examples in learning topological phases
• Learning Order Parameters from Videos of Dynamical Phases for Skyrmions with Neural Networks
• Thermometry of one-dimensional Bose gases with neural networks
• Learning entanglement breakdown as a phase transition by confusion
• Morphology of three-body quantum states from machine learning
• Statistical learning of engineered topological phases in the kagome superlattice of AV${}_3$Sb${}_5$
• Sparse Sampling for Fast Quasiparticle Interference Mapping
• Observing a topological phase transition with deep neural networks from experimental images of ultracold atoms
• Learning the Fuzzy Phases of Small Photonic Condensates
• Detecting composite orders in layered models via machine learning
• Adversarial Machine Learning Phases of Matter
• Machine Learning Identification of Impurities in the STM Images
• Iterative peak-fitting of frequency-domain data via deep convolution neural networks
• Efficient and quantum-adaptive machine learning with fermion neural networks
• Machine Learning approach to muon spectroscopy analysis
• Detecting Nematic Order in STM/STS Data with Artificial Intelligence
• Learning by Confusion: The Phase Diagram of the Holstein Model
• A learning algorithm with emergent scaling behavior for classifying phase transitions
• Superconducting gap symmetry from Bogoliubov quasiparticle interference analysis on {Sr}${}_2${RuO}${}_4$
• Classification of magnetic order from electronic structure by using machine learning
• Deep Learning Hamiltonians from Disordered Image Data in Quantum Materials
• Ground-state properties via machine learning quantum constraints
• Inferring Hidden Symmetries of Exotic Magnets from Detecting Explicit Order Parameters
• Deep learning extraction of band structure parameters from density of states: a case study on trilayer graphene
• Principal Component Analysis of Diffuse Magnetic Scattering: a Theoretical Study
• Preparing Quantum States by Measurement-feedback Control with Bayesian Optimization
• Fluctuation based interpretable analysis scheme for quantum many-body snapshots
• Single-shot quantum measurements sketch quantum many-body states
• Weighted Quantum Channel Compiling through Proximal Policy Optimization
• Many-body mobility edges in 1D and 2D revealed by convolutional neural networks
• Experimental kernel-based quantum machine learning in finite feature space
• Emergent channel over a pair of pockets in strong density waves
• Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
• Fast spectroscopic mapping of two-dimensional quantum materials
• Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
• Density-wave tendency from a topological nodal-line perspective