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Beyond Structural Insight: A Deep Neural Network for the Prediction of Pt L_2/3-edge X-ray Absorption Spectra

Lookup NU author(s): Dr Luke Watson, Conor Rankine, Professor Thomas Penfold

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Abstract

X-ray absorption spectroscopy at the L_2/3-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 L_2/3-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. ii) The post-edge, where oscillations encode the local geometric structure around the X-ray absorbtion 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 L_2/3- edge. We demonstrate that XANESNET is able to predict accurately Pt L_2/3-edge X-ray absorption spectra, including both the parts containing electronic and geometric structural information. The performance of our DNN in a practical situation is demonstrated by application to two Pt complexes and simulating the transient spectrum of a photoexcited dimeric Pt complex. Our discussion also 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.

Publication metadata

Author(s): Watson L, Rankine CD, Penfold TJ

Publication type: Article

Publication status: Published

Journal: Physical Chemistry Chemical Physics

Year: 2022

Volume: 24

Pages: 9156-9167

Online publication date: 31/03/2022

Acceptance date: 31/03/2022

Date deposited: 31/03/2022

ISSN (electronic): 1463-9084

Publisher: Royal Society of Chemistry

URL: https://doi.org/10.1039/D2CP00567K

DOI: 10.1039/D2CP00567K

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