Beyond structural insight: a deep neural network for the prediction of Pt L_2/3-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...

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

“…orbital information) should be included explicitly in the feature vector alongside the nuclear structural information is a natural one and, at present, one that requires further investigation. Watson et al [110] demonstrated that there remains a sufficiently strong implicit link between geometric and electronic structural information to develop a sufficiently accurate ML model at the Pt L 2/3 edges using a purely geometric feature vector viathe wACSF representation. The authors noted, however, that the error in the ML XAS spectral predictions was largest close to the L 2/3 absorption edges, i.e.…”

“…Figure 2 illustrates that x-ray spectra are typically broad in comparison to, for example, optical and vibrational spectroscopies. Consequently, the calculated spectra must be transformed by incorporating factors including core-hole-lifetime broadening and instrument response [110,133] to enable them to be compared to the experiment. An example of the influence this has is shown in figure 6 and can be added as a pre-processing or post-processing step in the ML models.…”

“…The focus of ML techniques applied to x-ray spectroscopy has, to date, largely been on the forward mapping problem. Here, in a manner akin to quantum chemistry calculations, an input structure is used to predict binding energies for photoemission [141][142][143], which is converted into the lineshape for XAS [92,110,125,[144][145][146][147][148] or XES [108,129,149]. These methods have addressed light and heavy elements (e.g.…”

A message passing neural network for predicting dipole moment dependent core electron excitation spectra

“…In this context, attempts have been made to predict spectra from molecular structures using machine learning models, and there have been several reports: prediction from molecular graphs using a message passing graph neural network (GNN), 12 a deep neural network, [13][14][15] and prediction using neural network ensemble through featurized local structural information. 16 However, as far as the authors know, the prediction of core electron excitation spectra considering anisotropy according to the momentum transfer has not been proposed.…”

An on-the-fly deep neural network for simulating time-resolved spectroscopy: predicting the ultrafast ring opening dynamics of 1,2-dithiane

“…We have previously developed 21,22 and deployed [25][26][27] a deep neural network (DNN) -XANESNET 28 -for predicting the lineshape of X-ray absorption (XAS) 21,22,29 and emission (XES) 23 spectra. XANESNET predicts spectral lineshapes using only local information about the coordination geometry of the absorbing atom.…”