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

ABSTRACT

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).

GRAPHICAL ABSTRACT

KEYWORDS:

• X-ray spectroscopy
• valence-to-core emission
• deep neural networks
• uncertainty and error
• transition metals

Acknowledgments

This paper is dedicated to the memory of Prof. Nick Besley.

Data availability

The data that support the findings of this study are openly available in Open Data Commons Open Database License at 10.25405/data.ncl.20134520.

Disclosure statement

There are no conflicts to declare.

Additional information

Funding

The research described in this paper was funded by the Leverhulme Trust (Project RPG-2020-268) and EPSRC (Engineering and Physical Sciences Research Council) (EP/S022058/1, EP/W008009/1, and EP/R51309X/1). This research made use of the Rocket High Performance Computing (HPC) service at Newcastle University.