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Lookup NU author(s): Conor Rankine, Dr Marwah Madkhali, Professor Thomas Penfold
This is the authors' accepted manuscript of an article that has been published in its final definitive form by American Chemical Society, 2020.
For re-use rights please refer to the publisher's terms and conditions.
X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end-users with highly-detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, e.g. in operando catalysts, batteries, and temporally-evolving systems, advanced theoretical calculations are necessary. The complexity and resource requirements often render these out of reach for end-users, and therefore data are often not interpreted exhaustively, leaving a wealth of valuable information unexploited. In this paper, we introduce supervised machine learning of X-ray absorption spectra, by developing a deep neural network (DNN) that is able to estimate Fe K-edge X-ray absorption near-edge structure spectra in less than a second with no input beyond geometric information about the local environment of the absorption site. We predict peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture. The performance of the DNN is promising, as illustrated by its application to the structural refinement of iron(II)tris(bipyridine) and nitrosylmyoglobin, but also highlights areas for which future developments should focus.
Author(s): Rankine CD, Madkhali MMM, Penfold TJ
Publication type: Article
Publication status: Published
Journal: Journal of Physical Chemistry A
Year: 2020
Volume: 124
Issue: 21
Pages: 4263-4270
Print publication date: 28/05/2020
Online publication date: 05/05/2020
Acceptance date: 05/05/2020
Date deposited: 09/05/2020
ISSN (print): 1089-5639
ISSN (electronic): 1520-5215
Publisher: American Chemical Society
URL: https://doi.org/10.1021/acs.jpca.0c03723
DOI: 10.1021/acs.jpca.0c03723
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