The dataset used in this study includes a diverse range of molecular structures, featuring 20 distinct functional groups and 20 distinct heavy atoms, including C, N, O, F, P, S, Cl, Br, Si, among others. The molecules have a mean molecular weight of 195.9 amu (σ = 90.4 amu), ranging from 25.0 to 1776.9 amu, and contain an average of 13.4 (σ = 6.4) heavy atoms. The dataset had a total of 6,833 molecules which were oversampled 3 times when training all models. As shown in Fig. , the most prevalent functional groups were alcohols, ethers, and aromatics, while enols, hydrazones, and imines were the least common. Due to their low occurrence, these latter groups were omitted from the j-IR-vis model to prevent class imbalance issues. Fig. (b) illustrates that the majority of molecules in the dataset contained an average of two functional groups. This distribution of functional groups and molecular complexity provided a robust foundation for training and evaluating the Spectro model, ensuring its ability to handle a wide variety of molecular structures commonly encountered in chemical analysis.