Bulletin of the American Physical Society
APS March Meeting 2023
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session D53: Machine Learning for Spectroscopy
3:00 PM–5:48 PM,
Monday, March 6, 2023
Room: Room 307
Sponsoring Unit: GDS
Chair: Nina Andrejevic, Argonne National Laboratory; Davis Unruh, Argonne National Laboratory
Abstract: D53.00001 : Teaching Core-Hole Spectroscopy to a Deep Neural Network
3:00 PM–3:36 PM
(University of York)
(University of York)
XANESNET is a DNN for instantaneous simulations of X-ray absorption spectra (XAS); the XANESNET Project is about addressing the challenge of delivering detailed, high- level theoretical simulations that can capture the complex underlying physics of these experiments but that are – at the same time – fast, affordable, and accessible enough to appeal to beamline users. Using DNNs like XANESNET, we can reduce the time taken to simulate XAS from hours/days to a fraction of a second, democratise data analysis, and enable beamline users to better plan beamtime allocations by facilitating ‘on-the-fly’, ‘real-time’ analysis. We’ve already deployed XANESNET in the practical
arena to take on open questions in physical and materials chemistry with success.
This talk will showcase how XANESNET delivers ‘black-box’, qualitative predictions of XAS at the transition metal K-edges using nothing more than the local geometries of arbitrary absorption sites, bypassing time- and resource-intensive quantum-chemical calculations and reproducing peak positions to sub-eV accuracy wrt. reference XAS data across the transition metal K-edges. Lifting the lid on XANESNET reveals that the predictions it produces are informed by the expected physics, giving us an insight into how, what, and why the model learns.
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