A deep neural network for valence-to-core X-ray emission spectroscopy
Thomas J. Penfold, Conor D. Rankine
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 ‘<i>held-out</i>’ 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).