Accurate, Affordable, and Generalisable Machine Learning Simulations of Transition Metal X-ray Absorption Spectra using the XANESNET Deep Neural Network

Abstract

The affordable, accurate, and reliable prediction of spectroscopic observables plays a key role in the analysis of increasingly-complex experiments. In this Article, we develop and deploy a deep neural network (DNN) – XANESNET – for predicting the lineshape of first-row transition metal K-edge X-ray absorption near-edge structure (XANES) spectra. XANESNET predicts the spectral intensities using only information about the local coordination geometry ofthe transition metal complexes encoded in a feature vector of weighted atom-centred symmetry functions (wACSF). We address in detail the calibration of the feature vector for the particularities of the problem at hand, and we explore the individual feature importances to reveal the physical insight that XANESNET obtains at the Fe K-edge. XANESNET relies on only a few judiciously-selected features – radial information on the first and second coordination shells suffices, along with angular information sufficient to separate satisfactorily key coordination geometries. The feature importance is found to reflect the XANES spectral window under consideration and is consistent with the expected underlying physics. We subsequently apply XANESNET at nine first-row transition metal (Ti–Zn) K-edges. It can be optimised in as little as a minute, predicts instantaneously, and provides K-edge XANES spectra with an average accuracy of ca. ± 2–4% in which the positions of prominent peaks are matched with a > 90% hit rate to sub-eV (ca.0.8 eV) error.