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RESEARCH PAPER · Digital Discovery · 2022

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

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

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16 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
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42 direct citing works

Machine learning enabling high-throughput and remote operations at large-scale user facilitiesOriginalLinking Scientific Instruments and HPC: Patterns, Technologies, Experiences8Advancing Discovery with Artificial Intelligence and Machine Learning at NSLS-II12When not to use machine learning: A perspective on potential and limitations51Linking scientific instruments and computation: Patterns, technologies, and experiences45Artificial intelligence for materials research at extremes20Delivering real-time multi-modal materials analysis with enterprise beamlines5Orchestrating nimble experiments across interconnected labs37Molecular-scale synchrotron X-ray investigations of solid-liquid interfaces in lithium-ion batteries1Flexible formulation of value for experiment interpretation and design6Event-driven data management with cloud computing for extensible materials acceleration platforms8Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy12Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach4Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning17Robotic integration for end-stations at scientific user facilities5Autonomous data collection for the neutron spin echo response function0Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy0

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16 direct citing papers saved.

  1. Linking Scientific Instruments and HPC: Patterns, Technologies, Experiences

    SSRN Electronic Journal · 2022-01-01

    Rafael Vescovi, Ryan Chard, Nickolaus Saint, Ben J. Blaiszik, Jim Pruyne, Tekin Biçer, Alex Lavens, Zhengchun Liu, Michael E. Papka, Suresh Narayanan, Nicholas Schwarz, Kyle Chard, Ian T Foster

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  2. Advancing Discovery with Artificial Intelligence and Machine Learning at NSLS-II

    Synchrotron Radiation News · 2022-07-04

    Andi Barbour, Stuart I. Campbell, Thomas A Caswell, Masafumi Fukuto, Marcus D. Hanwell, Andrew M. Kiss, Tatiana Konstantinova, Ricarda Laasch, Phillip M. Maffettone, Bruce Ravel, Daniel Olds

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  3. When not to use machine learning: A perspective on potential and limitations

    MRS Bulletin · 2022-09-01

    Matthew R. Carbone

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  4. Linking scientific instruments and computation: Patterns, technologies, and experiences

    Patterns · 2022-10-01

    Rafael Vescovi, Ryan Chard, Nickolaus Saint, Ben J. Blaiszik, Jim Pruyne, Tekin Biçer, Alex Lavens, Zhengchun Liu, Michael E. Papka, Suresh Narayanan, Nicholas Schwarz, Kyle Chard, Ian T Foster

    Read original paper ↗ ·

  5. Artificial intelligence for materials research at extremes

    MRS Bulletin · 2022-11-01

    Benji Maruyama, Jason R. Hattrick-Simpers, William D. Musinski, Lori L. Graham-Brady, Kang Li, Jonathan D. Hollenbach, A. Singh, Mitra L. Taheri

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

    Cell Reports Physical Science · 2022-11-01

    Phillip M. Maffettone, Stuart I. Campbell, Marcus D. Hanwell, Stuart B. Wilkins, Daniel Olds

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  7. Orchestrating nimble experiments across interconnected labs

    Digital Discovery · 2023-01-01

    Dan Guevarra, Kevin Kan, Yungchieh Lai, Ryan J. R. Jones, Lan Zhou, Phillip Donnelly, Matthias Hermann Richter, Helge Sören Stein, John M. Gregoire

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

    Elsevier eBooks · 2023-05-05

    Chuntian Cao, Hans‐Georg Steinrück

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  9. Flexible formulation of value for experiment interpretation and design

    Matter · 2023-12-12

    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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  10. Event-driven data management with cloud computing for extensible materials acceleration platforms

    Digital Discovery · 2023-12-21

    Michael J. Statt, Brian A. Rohr, Dan Guevarra, Santosh K. Suram, John M. Gregoire

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

    The Journal of Physical Chemistry A · 2024-02-28

    Matthew R. Carbone, Phillip M. Maffettone, Xiaohui Qu, Shinjae Yoo, Deyu Lu

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  12. Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach

    Machine Learning Science and Technology · 2024-04-23

    Cafer Tufan Cakir, Can Bogoclu, Franziska Emmerling, Christina Streli, Ana Guilherme Buzanich, Martin Radtke

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  13. Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning

    ACS Nano · 2024-08-26

    Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama Krishnan Vasudevan, Maxim A Ziatdinov, Sergei V. Kalinin

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  14. Robotic integration for end-stations at scientific user facilities

    Digital Discovery · 2025-01-01

    Chandima Fernando, Hailey Marcello, Jakub M. Wlodek, John Sinsheimer, Daniel Olds, Stuart I. Campbell, Phillip M. Maffettone

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  15. Autonomous data collection for the neutron spin echo response function

    APL Machine Learning · 2026-04-01

    Robert Huarcaya, Austin McDannald, William Ratcliff, David P. Hoogerheide

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  16. Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

    Advanced Intelligent Systems · 2026-08-30

    Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao

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