Universal rapid machine learning models for predicting unconvoluted and convoluted X-ray Absorption Spectra

TL;DR

This paper introduces a universal machine learning model capable of rapidly predicting both unconvoluted and convoluted X-ray Absorption Spectra from 3D molecular structures, enhancing analysis speed and accuracy across diverse XAS applications.

Contribution

The paper presents a novel, unified ML model for predicting XANES spectra from 3D structures, applicable to multiple elements and X-ray edges, with an efficient fitting algorithm for real-time analysis.

Findings

Model demonstrates high accuracy in predicting XANES spectra.

Applicable to both hard and soft X-ray XAS.

Enables online data analysis for XAS beamlines.

Abstract

X-ray absorption near edge structure (XANES) is an essential tool for elucidating the atomic-scale, local three-dimensional (3D) structure of given materials and molecules. The rapid computation of XANES based on molecular 3D structures constitutes a vital element of quantitative XANES analysis. Here, we present an XANES prediction model. It takes 3D structures as input and generates either unconvoluted XANES or convoluted spectra as output, demonstrating excellent generalizability across diverse instrumental broadening. This model has validated its predictive capability for both hard X-ray XAS (exemplified by K-edges of 3d 4d metals and lanthanides) and soft X-ray XAS (using S K-edge as examples). Adopting the model, XANES spectra of multiple elements can be predicted using a single unified model. A highly efficient 3D structure fitting algorithm based on this unconvoluted XANES prediction model, aiming to serve as an online data analysis method suitable for XAS beamlines.