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Lookup NU author(s): Clelia Middleton, Professor Thomas Penfold
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The performance of a Machine Learning (ML) algorithm for chemistry is highly contingent upon the architect’s choice of input representation. This work introduces the partial density of states (p-DOS) descriptor: a novel, quantum-inspired structural representation which encodes relevant electronic information for machine learning models seeking to simulate X-ray spectroscopy. p-DOS uses a minimal basis set in conjunction with a guess (non-optimised) electronic configuration to extract and then discretise the density of states (DOS) of the absorbing atom to form the input vector. We demonstrate that while the electronically-focused p-DOS performs well in isolation, optimal performance is achieved when supplemented with nuclear structural information imparted via a geometric representation. p-DOS provides a description of the key electronic properties of a system which is not only concise and computationally efficient, but also independent of molecular size or choice of basis set. It can be rapidly generated, facilitating its application with large training sets. Its performance is demonstrated using a wide variety of examples at the sulphur K-edge, including the prediction of ultrafast X-ray spectroscopic signal associated with photoexcited 2(5H)-thiophenone. These results highlight the potential for ML models developed using p-DOS to contribute to the interpretation and prediction of experimental results e.g. in operando measurements of batteries and/or catalysts and femtosecond time-resolved studies, especially those made possible by emergent cutting-edge technologies, especially X-ray free electron lasers.
Author(s): Middleton C, Curchod BFE, Penfold TJ
Publication type: Article
Publication status: Published
Journal: Physical Chemistry Chemical Physics
Year: 2024
Pages: Epub ahead of print
Online publication date: 30/08/2024
Acceptance date: 29/08/2024
Date deposited: 30/08/2024
ISSN (print): 1463-9076
ISSN (electronic): 1463-9084
Publisher: Royal Society of Chemistry
URL: https://doi.org/10.1039/D4CP01368A
DOI: 10.1039/D4CP01368A
Data Access Statement: Data supporting this publication is openly available. The software can be obtained from ref. 40, while the data can be obtained from ref. 49
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