[2P10] Development of an automatic spectral decomposition tool using reference data
To promote materials development based on data-driven science, it is essential to develop technologies for extracting features from high-dimensional measurement data such as images and spectra. There is a need for a system that automatically converts high-dimensional measurement data into features and stores the table data under AI-ready status. A cloud system, Research Data Express (RDE), is being developed to register experimental and computational data quickly. The RDE has workflows to automatically extract features from high-dimensional measurement data. As one of the feature extraction tools, we have developed an automatic spectral decomposition tool using reference data associated with physical states such as electron bound state and crystalline structure, etc. The developed tool can be used as part of the workflow functionality of the RDE. This spectral decomposition has high interpretability because it uses reference spectral data.
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