Beyond structural insight: a deep neural network for the prediction of Pt L 2/3 -edge X-ray absorption spectra

• Published in: Physical chemistry chemical physics : PCCP 2022-04, Vol.24 (16), p.9156-9167
• Main Authors: Watson, Luke, Rankine, Conor D, Penfold, Thomas J
• Format: Article
• Language: English
• Subjects: Coordination Complexes - chemistry; Neural Networks, Computer; Transition Elements; X-Ray Absorption Spectroscopy; X-Rays
• ISSN: 1463-9076; 1463-9084
• DOI: 10.1039/D2CP00567K

X-ray absorption spectroscopy at the L 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 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 L edge. We demonstrate that XANESNET is able to predict Pt L -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.