Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy
Gespeichert in:
| Veröffentlicht in: | NPJ Computational Materials vol. 11, no. 1 (2025), p. 320-334 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , , , |
| Veröffentlicht: |
Nature Publishing Group
|
| Schlagworte: | |
| Online-Zugang: | Citation/Abstract Full Text Full Text - PDF |
| Tags: |
Keine Tags, Fügen Sie das erste Tag hinzu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3265686354 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2057-3960 | ||
| 024 | 7 | |a 10.1038/s41524-025-01771-7 |2 doi | |
| 035 | |a 3265686354 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274866 |2 nlm | ||
| 100 | 1 | |a Du, Ming |u Advanced Photon Source, Argonne National Laboratory, 60439, Lemont, IL, USA (ROR: https://ror.org/05gvnxz63) (GRID: grid.187073.a) (ISNI: 0000 0001 1939 4845) | |
| 245 | 1 | |a Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about the structure of XANES spectra. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15–20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after the absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method’s effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments, reducing the common errors of under- or over-sampling points near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time. | |
| 653 | |a Data collection | ||
| 653 | |a Temporal resolution | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Oxidation | ||
| 653 | |a Neutrons | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Fourier transforms | ||
| 653 | |a X ray absorption | ||
| 653 | |a Sampling methods | ||
| 653 | |a Adaptive sampling | ||
| 653 | |a Optimization | ||
| 653 | |a Spectroscopy | ||
| 653 | |a Spectra | ||
| 653 | |a Energy | ||
| 653 | |a Automation | ||
| 653 | |a Error reduction | ||
| 653 | |a Algorithms | ||
| 653 | |a Sampling | ||
| 653 | |a X-ray spectroscopy | ||
| 653 | |a Expected values | ||
| 653 | |a X-rays | ||
| 700 | 1 | |a Wolfman, Mark |u Advanced Photon Source, Argonne National Laboratory, 60439, Lemont, IL, USA (ROR: https://ror.org/05gvnxz63) (GRID: grid.187073.a) (ISNI: 0000 0001 1939 4845) | |
| 700 | 1 | |a Sun, Chengjun |u Advanced Photon Source, Argonne National Laboratory, 60439, Lemont, IL, USA (ROR: https://ror.org/05gvnxz63) (GRID: grid.187073.a) (ISNI: 0000 0001 1939 4845) | |
| 700 | 1 | |a Kelly, Shelly D. |u Advanced Photon Source, Argonne National Laboratory, 60439, Lemont, IL, USA (ROR: https://ror.org/05gvnxz63) (GRID: grid.187073.a) (ISNI: 0000 0001 1939 4845) | |
| 700 | 1 | |a Cherukara, Mathew J. |u Advanced Photon Source, Argonne National Laboratory, 60439, Lemont, IL, USA (ROR: https://ror.org/05gvnxz63) (GRID: grid.187073.a) (ISNI: 0000 0001 1939 4845) | |
| 773 | 0 | |t NPJ Computational Materials |g vol. 11, no. 1 (2025), p. 320-334 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265686354/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3265686354/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265686354/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |