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RESEARCH PAPER · Applied Physics Reviews · 2021

Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experiments

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42 direct citing works

Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experimentsOriginalMachine Learning in Materials Science24Machine learning enabling high-throughput and remote operations at large-scale user facilities16nmfMapping : a cloud-based web application for non-negative matrix factorization of powder diffraction and pair distribution function datasets16Molecular Structure and Phase Equilibria of Molten Fluoride Salt with and without Dissolved Cesium: FLiNaK–CsF (5 mol %)15Advancing Discovery with Artificial Intelligence and Machine Learning at NSLS-II12Deciphering the structure of heterogeneous catalysts across scales using pair distribution function analysis34Why big data and compute are not necessarily the path to big materials science51Autonomous (AI-driven) materials science23Delivering real-time multi-modal materials analysis with enterprise beamlines5Synchrotron radiation based operando characterization of battery materials84Metric geometry tools for automatic structure phase map generation7Molecular-scale synchrotron X-ray investigations of solid-liquid interfaces in lithium-ion batteries1Flexible formulation of value for experiment interpretation and design6Exploring porous structures without crystals: advancements with pair distribution function in metal- and covalent organic frameworks21Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy12The rise of nonnegative matrix factorization: Algorithms and applications33Quantifying the cooperative evolution of microphase segregation and nanostructural order in annealed polyurethanes of MDI‐BDO‐PTHF4X-ray scattering based scanning tomography for imaging and structural characterization of cellulose in plants5Employing Constrained Nonnegative Matrix Factorization for Microstructure Segmentation4Implementation of simultaneous ultraviolet/visible and x-ray absorption spectroscopy with microfluidics2Distinguishing isotropic and anisotropic signals for X-ray total scattering using machine learning0Machine learning assisted nanobeam X-ray diffraction based analysis on hydride vapor-phase epitaxy GaN2Prostate cancer detection with blood serum autofluorescence: Comparison to non-optical methods2Oxidation Gradients in Brown-Glazed Ceramics from the Song Dynasty: Potential X-ray Markers of Provenance and Technology0Acid‐ and Nucleophile‐Gated Photoisomerization of Phosphaindirubin0Acid‐ and Nucleophile‐Gated Photoisomerization of Phosphaindirubin4Uncovering crystal structure evolution via nanobeam X-ray diffraction with a continuity-driven machine learning approach0Advancing Battery Diagnostics through Operando Neutron Diffraction of Unmodified Nondeuterated Li-Ion Single-Layer Pouch Cells2Multimodal Data‐Driven Microstructure Characterization0Real-time Multi-instrument Autonomous Materials Screening for Discovery of Novel Phase-change Memory Materials0TuckerHKN: multi-source hypergraph-guided Tucker co-learning for unraveling high-order structures in spatial transcriptomics0

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  2. Machine learning enabling high-throughput and remote operations at large-scale user facilities

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    Tatiana Konstantinova, Phillip M. Maffettone, Bruce Ravel, Stuart I. Campbell, Andi Barbour, Daniel Olds

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  3. nmfMapping : a cloud-based web application for non-negative matrix factorization of powder diffraction and pair distribution function datasets

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    Zachary Thatcher, Chia-Hao Liu, Long Yang, Brennan C. McBride, Gia Thinh Tran, Allison Wustrow, Dorthe Bomholdt Ravnsbæk, James Richmond Neilson, Dorthe Bomholdt Ravnsbæk, Simon J. L. Billinge

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  4. Molecular Structure and Phase Equilibria of Molten Fluoride Salt with and without Dissolved Cesium: FLiNaK–CsF (5 mol %)

    ACS Applied Energy Materials · 2022-06-28

    David Sprouster, Guiqiu Zheng, Shao-Chun Lee, Daniel Olds, Can Agca, Joanna McFarlane, Yang Zhang, Boris Khaykovich

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  6. Deciphering the structure of heterogeneous catalysts across scales using pair distribution function analysis

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    Nora K. Zimmerli, Christoph Rüdiger Müller, Paula M. Abdala

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  7. Why big data and compute are not necessarily the path to big materials science

    Communications Materials · 2022-08-30

    Naohiro Fujinuma, Brian DeCost, Jason R. Hattrick-Simpers, S. E. Lofland

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  9. Delivering real-time multi-modal materials analysis with enterprise beamlines

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  12. Molecular-scale synchrotron X-ray investigations of solid-liquid interfaces in lithium-ion batteries

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  13. Flexible formulation of value for experiment interpretation and design

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    Matthew R. Carbone, Hyeong Jin Kim, Chandima Fernando, Shinjae Yoo, Daniel Olds, Howie Joress, Brian DeCost, Bruce Ravel, Yugang Zhang, Phillip M. Maffettone

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  14. Exploring porous structures without crystals: advancements with pair distribution function in metal- and covalent organic frameworks

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    Ignacio Romero‐Muñiz, Edward Loukopoulos, Ying Xiong, Félix Zamora, Ana Eva Platero‐Prats

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  15. Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy

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  16. The rise of nonnegative matrix factorization: Algorithms and applications

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  17. Quantifying the cooperative evolution of microphase segregation and nanostructural order in annealed polyurethanes of MDI‐BDO‐PTHF

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    Maxwell W. Terban, Elmar Pöselt, Marc Malfois, Armin Schulz, Edgar Schander, Milinda Abeykoon, Bernd Hinrichsen, Robert E. Dinnebier

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  18. X-ray scattering based scanning tomography for imaging and structural characterization of cellulose in plants

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  19. Employing Constrained Nonnegative Matrix Factorization for Microstructure Segmentation

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    Ashish Chauniyal, P. Thomé, Markus Stricker

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  20. Implementation of simultaneous ultraviolet/visible and x-ray absorption spectroscopy with microfluidics

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  24. Oxidation Gradients in Brown-Glazed Ceramics from the Song Dynasty: Potential X-ray Markers of Provenance and Technology

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  27. Uncovering crystal structure evolution via nanobeam X-ray diffraction with a continuity-driven machine learning approach

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