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A deep neural network for valence-to-core X-ray emission spectroscopy

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



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


In this Article, we extend our XANESNET deep neural network (DNN) to predict the lineshape of first-row transition metal K-edge valence-to-core X-ray emission (VtC-XES) spectra. We demonstrate that – despite the strong sensitivity of VtC-XES to the electronic structure of the system under study – the DNN can reproduce the main spectral features from only the local coordination geometry of the transition metal complexes when encoded as a feature vector of weighted atom-centred symmetry functions (wACSF). We subsequently implement and evaluate three methods for assessing uncertainty in the predictions made by the VtC-DNN: deep ensembles, Monte-Carlo dropout, and bootstrap resampling. We show that bootstrap resampling provides the best performance when evaluated on ‘held-out’ testing data, and also demonstrates a strong correlation between the uncertainty it predicts and the error occurring between the target and predicted VtC-XES spectra. Finally, we demonstrate practical performance by application to unseen transition metal complexes across the entire first-row (Ti–Zn).

Publication metadata

Author(s): Penfold TJ, Rankine CD

Publication type: Article

Publication status: Published

Journal: Molecular Physics

Year: 2022

Volume: Memorial Issue for Nick Besley

Online publication date: 23/09/2022

Acceptance date: 03/09/2022

Date deposited: 23/09/2022

ISSN (print): 0026-8976

ISSN (electronic): 1362-3028

Publisher: Taylor & Francis


DOI: 10.1080/00268976.2022.2123406


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Funder referenceFunder name
Leverhulme Trust