A Graph Neural Network-Based Approach to XANES Data Analysis.

Abstract

The determination of three-dimensional structures (3D structures) is crucial for understanding the correlation between the structural attributes of materials and their functional performance. X-ray absorption near edge structure (XANES) is an indispensable tool to characterize the atomic-scale local 3D structure of the system. Here, we present an approach to simulate XANES based on a customized 3D graph neural network (3DGNN) model, XAS3Dabs, which takes directly the 3D structure of the system as input, and the inherent relation between the fine structure of spectrum and local geometry is considered during the model construction. It turns out to be faster than the traditional XANES fitting method when the simulation approach and XANES optimization algorithm are combined to fit the 3D structure of the given system. The geometric features of the system are included in the weighted message passing block of XAS3Dabs and their importance is investigated. XAS3Dabs model demonstrates superior accuracy in XANES prediction compared to most machine learning models. By extracting graphs constituted by edges related to the absorbing atom, our model reduces redundant information, thereby not only enhancing the model's performance but also improving its robustness across different hyperparameters. XAS3Dabs model can be generalized to simulate the spectra for the systems with the absorber having the designed absorption edge so as to meet the expectations of online data processing. The method is expected to be the key part of the online 3D structure analysis framework for the XAS-related beamlines of high-energy photon source (HEPS) now under construction.