Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction

Abstract

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern x-ray facilities have allowed a dramatically increased fraction of this information to be captured. Now, the primary challenge is to understand and discover scientific principles from big data sets when a comprehensive analysis is beyond human reach. We report the development of a novel unsupervised machine learning approach, XRD Temperature Clustering (X-TEC), that can automatically extract charge density wave (CDW) order parameters and detect intra-unit cell (IUC) ordering and its fluctuations from a series of high-volume X-ray diffraction (XRD) measurements taken at multiple temperatures. We apply X-TEC to XRD data on a quasi-skutterudite family of materials, (Ca${}_x$Sr${}_{1-x}$)${}_3$Rh${}_4$Sn${}_{13}$, where a quantum critical point arising from charge order is observed as a function of Ca concentration. We further apply X-TEC to XRD data on the pyrochlore metal, Cd${}_2$Re${}_2$O${}_7$, to investigate its two much debated structural phase transitions and uncover the Goldstone mode accompanying them. We demonstrate how unprecedented atomic scale knowledge can be gained when human researchers connect the X-TEC results to physical principles. Specifically, we extract from the X-TEC-revealed selection rule that the Cd and Re displacements are approximately equal in amplitude, but out of phase. This discovery reveals a previously unknown involvement of $5d^2$ Re, supporting the idea of an electronic origin to the structural order. Our approach can radically transform XRD experiments by allowing in-operando data analysis and enabling researchers to refine experiments by discovering interesting regions of phase space on-the-fly.

References in corpus (31)

• Nematic Fermi Fluids in Condensed Matter Physics
• Intra-unit-cell electronic nematicity of the high-Tc copper-oxide pseudogap states
• Spatially modulated "Mottness" in La2-xBaxCuO4
• Superconductivity at 1 K in Cd2Re2O7
• Intertwined vestigial order in quantum materials: nematicity and beyond
• Machine Learning in Electronic Quantum Matter Imaging Experiments
• Parity-breaking phases of spin-orbit-coupled metals with gyrotropic, ferroelectric and multipolar orders
• Spin-Peierls phases in pyrochlore antiferromagnets
• Integrating Neural Networks with a Quantum Simulator for State Reconstruction
• A parity-breaking electronic nematic phase transition in the spin-orbit coupled metal Cd${}_2$Re${}_2$O${}_7$
• Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
• Superconductivity in the Correlated Pyrochlore Cd_2Re_2O_7
• Machine Learning on Neutron and X-Ray Scattering
• Classifying Snapshots of the Doped Hubbard Model with Machine Learning
• Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
• Ambient Pressure Structural Quantum Critical Point in the Phase Diagram of (Ca${}_x$Sr${}_{1-x}$)${}_3$Rh${}_4$Sn${}_{13}$
• Low Temperature Symmetry of Pyrochlore Oxide Cd2Re2O7
• Electric Toroidal Quadrupoles in Spin-Orbit Coupled Metal Cd${}_2$Re${}_2$O${}_7$
• Pyrochlore Oxide Superconductor Cd2Re2O7 Revisited
• Goldstone-Mode Phonon Dynamics in the Pyrochlore Cd2Re2O7
• Structural Ordering and Symmetry Breaking in Cd_2Re_2O_7
• Structural Order Parameter in the Pyrochlore Superconductor Cd2Re2O7
• One-Component Order Parameter in URu${}_2$Si${}_2$ Uncovered by Resonant Ultrasound Spectroscopy and Machine Learning
• The nature of the tensor order in Cd2Re2O7
• Evidence of an improper displacive phase transition in Cd${}_2$Re${}_2$O${}_7$ via time-resolved coherent phonon spectroscopy
• Successive spatial symmetry breaking under high pressure in the spin-orbit-coupled metal Cd2Re2O7
• Evidence of a structural quantum critical point in (Ca${}_x$Sr${}_{1-x}$)${}_3$Rh${}_4$Sn${}_{13}$ from a lattice dynamics study
• Manifestion of structural Higgs and Goldstone modes in the hexagonal manganites
• Crystal structure of the inversion-breaking metal Cd2Re2O7
• Low-Temperature Phase of the Cd${}_2$Re${}_2$O${}_7$ Superconductor: Ab initio Phonon Calculations and Raman Scattering
• Unveiling charge density wave quantum phase transitions by x-ray diffraction

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• Structural evolution of the kagome superconductors $A$V${}_3$Sb${}_5$ ($A$ = K, Rb, and Cs) through charge density wave order
• On-the-fly Autonomous Control of Neutron Diffraction via Physics-Informed Bayesian Active Learning
• Machine learning discovery of new phases in programmable quantum simulator snapshots
• Frustrated charge order and cooperative distortions in ScV6Sn6
• Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation
• Bragg glass signatures in Pd${}_x$ErTe${}_3$ with X-ray diffraction Temperature Clustering (X-TEC)
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
• DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer
• Phase-Separated Charge Order and Twinning Across Length Scales in CsV${}_3$Sb${}_5$
• Diffuse Scattering from Correlated Electron Systems
• Successive phase transitions of the spin-orbit-coupled metal Cd2Re2O7 probed by high-resolution synchrotron x-ray diffraction
• Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons
• Leggett Modes Accompanying Crystallographic Phase Transitions
• Local structure and its implications for the relaxor ferroelectric Cd${}_2$Nb${}_2$O${}_7$