Computational X-ray Absorption Spectroscopy in Complex Materials: Advanced Ab Initio Approaches and Machine Learning Techniques

INVITED · Z56 · ID: 1853196

Presentations

  • Many-body corrections in self-consistent field approaches to X-ray spectroscopy simulations

    ORAL · Invited

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    Publication: [1] Yufeng Liang, John Vinson, S. C. Pemmaraju, Walter S. Drisdell, Eric L. Shirley, David Prendergast, Phys. Rev. Lett. 118, 096402 (2017).
    [2] Yufeng Liang and David Prendergast, Physical Review B 97, 205127 (2018).
    [3] Subhayan Roychoudhury, Leonardo A. Cunha, Martin Head-Gordon, and David Prendergast Phys. Rev. B 106, 075133 (2022).
    [4] Subhayan Roychoudhury and David Prendergast, Phys. Rev. B 106, 115115 (2022).
    [5] Subhayan Roychoudhury and David Prendergast, Phys. Rev. B 107, 035146 (2023)

    Presenters

    • David Prendergast

      Lawrence Berkeley National Laboratory

    Authors

    • David Prendergast

      Lawrence Berkeley National Laboratory

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  • Towards a new paradigm for machine learning-driven analysis and surrogate modeling for X-ray absorption spectroscopy

    ORAL · Invited

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    Publication: H. Kwon, W. Sun, T. Hsu, W. Jeong, F. Aydin, S. Sharma, F. Meng, M. R. Carbone, X. Chen, D. Lu, L. F. Wan, M. H. Nielsen & T. A. Pham. Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon. The Journal of Physical Chemistry C 127, 16473 (2023).

    M. R. Carbone, F. Meng, C. Vorwerk, B. Maurer, F. Peschel, X. Qu, E. Stavitski, C. Draxl, J. Vinson & D. Lu. Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files. The Journal of Open Source Software 8, 5182 (2023).

    A. Ghose, M. Segal, F. Meng, Z. Liang, M. S. Hybertsen, X. Qu, E. Stavitski, S. Yoo, D. Lu & M. R. Carbone. Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles. Physical Review Research 5, 013180 (2023).

    S. B. Torrisi, M. R. Carbone, B. A. Rohr, J. H. Montoya, Y. Ha, J. Yano, S. K. Suram & L. Hung.. Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships. npj Computational Materials 6, 109 (2020).

    M. R. Carbone, M. Topsakal, D. Lu & S. Yoo. Machine-learning X-ray absorption spectra to quantitative accuracy. Physical Review Letters 124, 156401 (2020).

    M. R. Carbone, S. Yoo, M. Topsakal & D. Lu. Classification of local chemical environments from x-ray absorption spectra using supervised machine learning. Physical Review Materials 3, 033604 (2019).

    Presenters

    • Matthew R Carbone

      Brookhaven National Laboratory

    Authors

    • Matthew R Carbone

      Brookhaven National Laboratory

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  • Modeling multiplet effects in ‭X-ray spectroscopies

    ORAL · Invited

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    Presenters

    • Maurits W Haverkort

      Heidelberg University, Institute of theoretical physics Heidelberg University, Institute for Theoretical Physics, Heidelberg University

    Authors

    • Maurits W Haverkort

      Heidelberg University, Institute of theoretical physics Heidelberg University, Institute for Theoretical Physics, Heidelberg University

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