However, data collection is considerably challenging in fields wherein extensive data (such as the Cambridge Crystallographic Data Center and Spectral Database for Organic Compounds (SDBS) 4 ) have not yet been compiled. Therefore, preparing spectral data through quantum chemical simulations for ML is a popular alternative . However, this approach also presents a challenge in that the outputs of these simulations are based on methods that are not fully established, and scientists use existing knowledge to perform ML. Furthermore, the use of neural networks results in the black-box problem, which makes it difficult for scientists to determine causal relationships. In such cases, it is impossible to obtain entirely new information, and from the perspective of materials science, there is an apprehension that the results may deviate from the actual properties of the materials.