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RESEARCH PAPER · Digital Discovery · 2023

Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data

Andy S. Anker, Keith T. Butler, Manh Duc Le, T. G. Perring, Jeyan Thiyagalingam

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

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42 direct citing works

Using generative adversarial networks to match experimental and simulated inelastic neutron scattering dataOriginalMachine learning for analysis of experimental scattering and spectroscopy data in materials chemistry52Materials cartography: A forward-looking perspective on materials representation and devising better maps13Capturing dynamical correlations using implicit neural representations11Synthetic pre-training for neural-network interatomic potentials20Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models18Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy12Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning1Small angle neutron scattering in McStas: Optimization for high-throughput virtual experiments1On ultrafast x-ray scattering methods for magnetism3The amorphous state as a frontier in computational materials design74A shift-invariant deep learning framework for automated analysis of XPS spectra1

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  1. Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

    Chemical Science · 2023-01-01

    Andy S. Anker, Keith T. Butler, Raghavendra Selvan, Kirsten M. Ø. Jensen

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  2. Materials cartography: A forward-looking perspective on materials representation and devising better maps

    APL Machine Learning · 2023-06-01

    Steven B. Torrisi, Martin Z. Bazant, Alexander E. Cohen, Min Gee Cho, Jens S. Hummelshøj, Linda Hung, Gaurav Ashish Kamat, Arash Khajeh, Adeesh Kolluru, Xiangyun Lei, Handong Ling, Joseph H. Montoya, Tim Mueller, Aini Palizhati, Benjamin Paren, Brandon K. Phan, Jacob Pietryga, Elodie Sandraz, Daniel Schweigert, Yang Shao‐Horn, Amalie Trewartha, Ruijie Zhu, Debbie Zhuang, Shijing Sun

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  3. Capturing dynamical correlations using implicit neural representations

    Nature Communications · 2023-09-20

    Sathya R. Chitturi, Zhurun Ji, Alexander N. Petsch, Cheng Peng, Zhantao Chen, Rajan Plumley, Mike Dunne, Sougata Mardanya, Sugata Chowdhury, Hongwei Chen, Arun Bansil, Adrian E. Feiguin, Alexander I. Kolesnikov, D. Prabhakaran, S. M. Hayden, Daniel F. Ratner, Chunjing Jia, Youssef S. G. Nashed, Joshua J. Turner

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  4. Synthetic pre-training for neural-network interatomic potentials

    Machine Learning Science and Technology · 2023-12-15

    John L. A. Gardner, Kathryn T. Baker, Volker L. Deringer

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  5. Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models

    The Journal of Physical Chemistry Letters · 2024-02-12

    Zhiwen Zhu, Jiayi Lu, Shaoxuan Yuan, Yu He, Fengru Zheng, Hao Jiang, Yuyi Yan, Qiang Sun

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  6. Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

    Machine Learning Science and Technology · 2024-05-24

    Thomas J. Penfold, Luke Watson, Clelia Middleton, Tudur Wyn David, Sneha Verma, Thomas J. Pope, Julia Kaczmarek, Conor D. Rankine

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  7. Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

    Machine Learning Science and Technology · 2024-09-01

    Yaokun Su, Chen W. Li

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  8. Small angle neutron scattering in McStas: Optimization for high-throughput virtual experiments

    Journal of Neutron Research · 2024-11-01

    José Ignacio Robledo, Klaus Lieutenant, Peter Kjær Willendrup

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  9. On ultrafast x-ray scattering methods for magnetism

    Advances in Physics X · 2024-11-24

    Rajan Plumley, Sathya R. Chitturi, Cheng Peng, Tadesse A. Assefa, Nicolas Burdet, L. Shen, Zhijiang Chen, Alexander H. Reid, Georgi L. Dakovski, Matthew Seaberg, Frank P. O'Dowd, Sergio A Montoya, H. Chen, Alana Okullo, Sougata Mardanya, S. D. Kevan, Peter Fischer, Eric E. Fullerton, Sunil Kumar Sinha, William Colocho, Alberto A. Lutman, F.-J. Decker, Sujoy Roy, Jun Fujioka, Y. Tokura, Michael P. Minitti, Jeremy A. Johnson, Matthias C. Hoffmann, Michaela Amoo, Adrian E. Feiguin, Chun Hong Yoon, Jana B. Thayer, Youssef S. G. Nashed, Chunjing Jia, Arun Bansil, S. Chowdhury, Aaron M. Lindenberg, Mike Dunne, E. Blackburn, Joshua J. Turner

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  10. The amorphous state as a frontier in computational materials design

    Nature Reviews Materials · 2024-12-16

    Yuanbin Liu, Ata Madanchi, Andy S. Anker, Lena Simine, Volker L. Deringer

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  11. A shift-invariant deep learning framework for automated analysis of XPS spectra

    Journal of Electron Spectroscopy and Related Phenomena · 2026-06-27

    Issa Saddiq, Yuxin Fan, Robert G. Palgrave, Mark Andrew Isaacs, David J. Morgan, Keith T. Butler

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