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Partial Density of States Representation for Accurate Deep Neural Network Predictions of X-ray Spectra

Lookup NU author(s): Clelia Middleton, Professor Thomas Penfold

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


Abstract

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.


Publication metadata

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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Funding

Funder referenceFunder name
COSMOS Programme grant (EPSRC grant no. EP/X026973/1)
EPSRC for an Open Fellowship (EP/W008009/1)
Leverhulme Trust (Project RPG-2020-268)
UK High-End Computing Consortium (EPSRC grant no. EP/X035514/1)

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