Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table

TL;DR

XAStruct is a versatile machine learning system that predicts X-ray Absorption Spectroscopy spectra from crystal structures and infers local atomic environments across the periodic table, reducing reliance on expert analysis and expensive simulations.

Contribution

It introduces the first generalizable ML approach for predicting neighbor atom types from XAS spectra and a regression model for mean nearest-neighbor distance without element-specific tuning.

Findings

Trained on data spanning over 70 elements, enabling broad applicability.

Successfully predicts local structural descriptors from XAS spectra.

Provides scalable, data-driven analysis tools for XAS interpretation.

Abstract

X-ray Absorption Spectroscopy (XAS) is a powerful technique for probing local atomic environments, yet its interpretation remains limited by the need for expert-driven analysis, computationally expensive simulations, and element-specific heuristics. Recent advances in machine learning have shown promise for accelerating XAS interpretation, but many existing models are narrowly focused on specific elements, edge types, or spectral regimes. In this work, we present XAStruct, a learning-based system capable of both predicting XAS spectra from crystal structures and inferring local structural descriptors from XAS input. XAStruct is trained on a large-scale dataset spanning over 70 elements across the periodic table, enabling generalization to a wide variety of chemistries and bonding environments. The framework includes the first machine learning approach for predicting neighbor atom types directly from XAS spectra, as well as a generalizable regression model for mean nearest-neighbor distance that requires no element-specific tuning. By combining deep neural networks for complex structure property mappings with efficient baseline models for simpler tasks, XAStruct offers a scalable and extensible solution for data-driven XAS analysis and local structure inference. The source code will be released upon paper acceptance.