Materials informatics involves the development of new functional materials, particularly drugs, which is typically a costly and labor-intensive process. However, material informatics attempts to address these issue through innovative advances by leveraging machine learning (ML) algorithms 1 . Gómez-Bombarelli et al. developed a method to convert discrete molecular representations into a continuous space, enabling molecule generation, exploration, and optimization through a deep neural network that encodes, decodes, and predicts properties in a latent space for efficient compound designing . Liu et al. proposed SDDSynergy, a method that predicts anticancer drug synergy by identifying key functional groups through drug-cell line and substructure pair attention mechanisms, thereby enhancing synergy prediction and outperforming conventional approaches on multiple datasets 1e . In addition, attempts have been made to extract trends in the prediction of physical properties by applying ML algorithms to spectral images, such as those obtained in X-ray absorption fine structure (XAFS) and Xray photoelectron spectroscopy 2 . Timoshenko et al. have reported that the tracking of the local structure of the heterogeneous materials is optional, whereas an extended XAFS database has revealed M-M or M-L (M: metal; L: ligand) distances or ratios of structures using neural network methods 2b,2c . Torrisi et al. simulated X-ray absorption spectroscopy (XAS) spectra using the FEFF software and employed random-forest ML models to analyze these XAS spectra, where the spectra allowed to predict the Bader charge 2d . Therefore, using artificial intelligence (AI)-based methods for spectral analysis allows visualizing features that were previously described by scientists based on their intuitions.