Computational X-ray Absorption Spectroscopy in Complex Materials: Advanced Ab Initio Approaches and Machine Learning Techniques
INVITED · Z56 · ID: 1853196
Presentations
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Many-body effects in the X-ray absorption spectra of liquid water
ORAL · Invited
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Publication: Proc. Natl. Acad. Sci. U.S.A., 2022, 119, e2201258119
Presenters
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Fujie Tang
- Temple University
Authors
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Fujie Tang
- Temple University
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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
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David Prendergast
- Lawrence Berkeley National Laboratory
Authors
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David Prendergast
- Lawrence Berkeley National Laboratory
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Practical, reliable, and large-scale calculations of x-ray spectra using the Bethe-Salpeter equation method
ORAL · Invited
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Presenters
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John Vinson
- National Institute of Standards and Tech
Authors
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John Vinson
- National Institute of Standards and Tech
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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
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Matthew R Carbone
- Brookhaven National Laboratory
Authors
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Matthew R Carbone
- Brookhaven National Laboratory
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Modeling multiplet effects in X-ray spectroscopies
ORAL · Invited
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Presenters
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Maurits W Haverkort
- Heidelberg University
- Institute of theoretical physics Heidelberg University
- Institute for Theoretical Physics, Heidelberg University
Authors
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Maurits W Haverkort
- Heidelberg University
- Institute of theoretical physics Heidelberg University
- Institute for Theoretical Physics, Heidelberg University
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