Toggle Main Menu Toggle Search

Open Access padlockePrints

Enhancing the Analysis of Disorder in X-Ray Absorption Spectra: Application of Deep Neural Networks to T-jump-X-ray Probe Experiments

Lookup NU author(s): Dr Marwah Madkhali, Conor Rankine, Professor Thomas Penfold

Downloads


Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

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.


Publication metadata

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


Altmetrics

Altmetrics provided by Altmetric


Share