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

抄録

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.

Fig. 1 Schematic drawing of the neural network model that predicts structure descriptors from XANES spectra.