Enhancing the analysis of disorder in X-ray absorption spectra: application of deep neural networks to T-jump-X-ray probe experiments

Abstract

Many chemical and biological reactions, including ligand exchange processes, require thermal energy for the reactants to overcome a transition barrier and reach the product state. Temperature-jump (T-jump) spectroscopy uses a...

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.…”

“…We have demonstrated that, using a small training dataset of 6000 local absorption sites and Pt L 2/3 -edge XANES spectra, it is possible to develop a DNN with a simple architecture, very similar to our previous transition metal K-edge DNNs, 43,47 which is effective for predicting Pt L 2/3 edge XANES spectra, as demonstrated on 530 held-out testing dataset samples. However, a key difference from our previous work 43–47 at the transition metal K edges is that, here, our DNN is ostensibly unable to learn from absorption cross-sections without preprocessing since the L 2/3 edge exhibits very structured, sharp resonance peaks characteristic of bound-bound transitions. This challenge is addressable by applying either a fixed-width Lorentzian or arctangent convolutional broadening to each calculated L 2/3 edge XANES spectrum prior to training.…”

“…28–42 In particular, and the focus of this Article, the X-ray regime provides valuable element- and site-specific information on the geometric, electronic, and spin structure of matter and, consequently, a number of recent works have addressed directly the development of models for the prediction and analysis of X-ray absorption spectra. 43–65…”

“…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.…”

Accurate, Affordable, and Generalisable Machine Learning Simulations of Transition Metal X-ray Absorption Spectra using the XANESNET Deep Neural Network

“…a "fingerprint" window sensitive to a particular property or observable) and developing a machine learning model to predict directly the resonances within this window; 48,49,[98][99][100][101][102][103] ii) representing the resonances via a Hamiltonian matrix associated with a closed set of secular equations and developing a machine learning model to predict the Hamiltonian matrix elements; 27,39,41,42,50 and iii) developing a machine learning model to predict directly the spectral lineshapes. [71][72][73][74]104,105 The latter approach, which we adopt in this Article and elsewhere where we have worked with machine learning models for XAS in theoretical 71 and practical 73,74 settings, circumvents the formidable challenge of predicting the huge number of resonances around the Xray absorption edge. 106 Sitting alongside the well-developed theory for XAS (e.g.…”