Accurate, affordable, and generalizable 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 of the 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. XANES- NET 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. {plus minus} 2-4% in which the positions of prominent peaks are matched with a > 90% hit rate to sub-eV (ca. 0.8 eV) error.

Beyond structural insight: a deep neural network for the prediction of Pt L2/3-edge X-ray absorption spectra

“…In this Article, we have developed and applied a DNN to predict accurately and affordably Pt L 2/3 -edge XANES spectra. In contrast to our previous work [43][44][45][46][47] at the transition metal K edges, the L 2/3 -edge XANES spectra exhibit a larger and more direct sensitivity to the electronic structure of the absorbing atom owing to the dipole-allowed bound-bound 5d ' 2p electronic transitions. In contrast, at the transition metal K-edges, electronic transitions into the unoccupied d-DOS are dipole forbidden and form only a weak pre-edge region in the K-edge XANES spectra.…”

“…The general architecture of the current-generation XANESNET DNN is described in detail in ref. 47 we recommend this reference to the unfamiliar reader. The code is publicly available under the GNU Public License (GPLv3) on GitLab.…”

“…To address this, we have recently developed 43,47 and applied 45,46 a deep neural network (DNN) based on the simple multilayer perceptron (MLP) model that has been able to model accurately and affordably the K-edge X-ray absorption spectra of transition metal complexes. Our DNN predicts peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture from no input beyond geometric information about the local environment of the X-ray absorption site.…”

“…Our DNN predicts peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture from no input beyond geometric information about the local environment of the X-ray absorption site. 43,47 With the ability to predict ca. 1000 XANES spectra per second using off-the-shelf commercial-grade hardware (an AMD Ryzen Threadripper 3970X CPU; 3.7-4.5 GHz), our DNN provides a significant reduction in computational expense compared to first-principles/ab initio simulations that facilitates large-scale configurational sampling.…”

“…In this Article, we extend our recently-developed DNN 43,47 beyond the K-edge to predict XANES spectra at the Pt L 2/3 edge with a view towards exploring whether it is possible for the present framework to predict accurately the electronic, as well as the structural, features of the L 2/3 edge. Importantly, although there has been significant development in the firstprinciples/ab initio treatment of L 2/3 edge XANES spectra, [66][67][68][69][70][71][72][73] it remains a challenging task to develop (statistical) model-based approaches.…”