Extraction of Local Structure Information from X-ray Absorption Near-Edge Structure: A Machine Learning Approach

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  • Higashi Megumi
    Department of Materials Science, Graduate School of Engineering, Osaka Metropolitan University
  • Ikeno Hidekazu
    Department of Materials Science, Graduate School of Engineering, Osaka Metropolitan University

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<p>In this work, we constructed machine learning models to predict structural descriptors that numerically represent the atomic structures in three dimensions from x-ray absorption near-edge structure (XANES) spectra. The neural network models that predict radial distribution functions (RDF) and orbital-field matrix (OFM), a descriptor that deals with the anisotropy of the local structure, the valence electron number of the ligand, and orbital information, were constructed. We used more than 120,000 O K-edge XAS spectra data from the Materials Project database as the training data set. We successfully constructed models that roughly predicted RDFs with 74% of the test data. Furthermore, the model that predicted OFM also captured an overview of OFM in 97% of the test data. These results demonstrate that the atomic structural information can be directly extracted from XANES spectra using neural network models.</p>

Journal

  • MATERIALS TRANSACTIONS

    MATERIALS TRANSACTIONS 64 (9), 2179-2184, 2023-09-01

    The Japan Institute of Metals and Materials

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