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Lookup NU author(s): Dr Marwah Madkhali, Conor Rankine, Professor Thomas Penfold
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Many chemical and biological reactions, including ligand exchange processes, require thermal energy for the reactants to overcome a transition barrier and reach the product state. Temperature-jump (T-jump) spectroscopy uses a near-infrared (NIR) pulse to rapidly heat a sample, offering an approach for triggering these processes and directly accessing thermally-activated pathways. However, thermal activation inherently increases the disorder of the system under study and, as a consequence, can make quantitative interpretations of structural changes challenging. In this Article, we optimise a deep neural network (DNN) for the instantaneous prediction of Co K-edge X-ray absorption near- edge structure (XANES) spectra. We apply our DNN to analyse T-jump pump/X-ray probe data pertaining to the ligand exchange processes and solvation dynamics of Co2+ in chlorinated aqueous solution. Our analysis is greatly facilitated by machine learning, as our DNN is able to predict quickly and cost-effectively the XANES spectra of thousands of geometric configurations sampled from ab initio molecular dynamics (MD) using nothing more than the local geometric environment around the X-ray absorption site. We identify directly the structural changes following the T-jump, which are dominated by sample heating and a commensurate increase in the Debye-Waller factor.
Author(s): Madkhali MMM, Rankine CD, Penfold TJ
Publication type: Article
Publication status: Published
Journal: Physical Chemistry Chemical Physics
Year: 2021
Volume: 23
Issue: 15
Pages: 9259-9269
Online publication date: 30/03/2021
Acceptance date: 30/03/2021
Date deposited: 30/03/2021
ISSN (print): 1463-9076
ISSN (electronic): 1463-9084
Publisher: Royal Society of Chemistry
URL: https://doi.org/10.1039/D0CP06244H
DOI: 10.1039/D0CP06244H
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