Issue 16, 2022

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

Abstract

X-ray absorption spectroscopy at the L2/3 edge can be used to obtain detailed information about the local electronic and geometric structure of transition metal complexes. By virtue of the dipole selection rules, the transition metal L2/3 edge usually exhibits two distinct spectral regions: (i) the “white line”, which is dominated by bound electronic transitions from metal-centred 2p orbitals into unoccupied orbitals with d character; the intensity and shape of this band consequently reflects the d density of states (d-DOS), which is strongly modulated by mixing with ligand orbitals involved in chemical bonding, and (ii) the post-edge, where oscillations encode the local geometric structure around the X-ray absorption site. In this Article, we extend our recently-developed XANESNET deep neural network (DNN) beyond the K-edge to predict X-ray absorption spectra at the Pt L2/3 edge. We demonstrate that XANESNET is able to predict Pt L2/3 -edge X-ray absorption spectra, including both the parts containing electronic and geometric structural information. The performance of our DNN in practical situations is demonstrated by application to two Pt complexes, and by simulating the transient spectrum of a photoexcited dimeric Pt complex. Our discussion includes an analysis of the feature importance in our DNN which demonstrates the role of key features and assists with interpreting the performance of the network.

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

Supplementary files

Article information

Article type
Paper
Submitted
03 Febr. 2022
Accepted
31 Marts 2022
First published
31 Marts 2022
This article is Open Access
Creative Commons BY license

Phys. Chem. Chem. Phys., 2022,24, 9156-9167

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

L. Watson, C. D. Rankine and T. J. Penfold, Phys. Chem. Chem. Phys., 2022, 24, 9156 DOI: 10.1039/D2CP00567K

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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