Note. The AI model does not know the chemistry of materials and only learns the images. In addition, we evaluated the results and inferred the decisions made by the AI model; however, the actual decisions were obscured by the black-box problem, as in other ML studies. Moreover, technical issues with the measurements were not considered (this condition remained unchanged even if the data were plotted). Dataset and Processing. The target compounds were characterized by various functional groups, ranging from low to medium organic molecules. To verify their effectiveness, we used 230 compounds (some pairs had different numbers in the SDBS, but the same compounds), which included inorganic compounds and medium molecules with molecular weights ranging from 200-400. The search function of SDBS was used to randomly select compounds containing C and H; C, H, and N; and C, H, N, and O. For comparisons, we also included small molecules with molecular weights of 100 or less, but the number was small. Therefore, they could not be clearly classified. However, the method demonstrated strong performance in classifying compounds based solely on IR spectra, highlighting its potential as an effective tool for chemical classification. Many of these molecular structures are complicated, and it was assumed that it would be relatively difficult for chemists to distinguish between them based on IR spectra alone. The results are summarized in Table .