A new framework for X-ray absorption spectroscopy data analysis based on machine learning: XASDAML

X-ray absorption spectroscopy (XAS) is a critical analytical technique for comprehensively characterizing the electronic configurations and atomic structures of materials. The rapid growth in both data volume and complexity, driven by modern synchrotron radiation facilities, necessitates computational frameworks capable of efficiently processing large-scale XAS datasets. To address this need, we introduce XASDAML, a machine-learning-based platform that integrates the entire data processing workflow. The framework coordinates key operational processes, including spectral-structural descriptor generation, predictive modeling and performance validation, while facilitating statistical analyses through principal component decomposition and clustering algorithms to uncover latent patterns within datasets. Designed with modular architecture, the system enables independent modification or enhancement of individual components, ensuring flexibility to meet evolving analytical demands. Implemented through a Jupyter Notebook-based interface, the platform ensures accessibility for researchers. The framework is validated with two case studies: (i) copper-foil EXAFS data show that it can predict coordination numbers and radial distribution functions; and (ii) XANES spectra of the spin-crossover complex Fe(phen)₃ uncover bond-length changes between the low-spin and high-spin states. Comprehensive validation highlights robust toolkit functionalities, including statistical descriptor analyses, spectral visualization, and prediction of widely employed structural descriptors closely reflecting local atomic environments. By establishing standardized and extensible procedures for integrating machine learning into XAS analysis, XASDAML enhances research efficiency, promotes richer data insights, and provides a versatile computational resource tailored to the expanding needs of XAS research.