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

ABSTRACT: 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

SUBMITTER: Han X 

PROVIDER: S-EPMC12416422 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature