Machine learning for spectral dataset analysis to improve structure identification accuracy and speed is common using Nuclear Magnetic Resonance (NMR), Infrared Spectroscopy (IR), and Mass Spectrometry (MS). Researchers training these models typically require expertise on the data being used, the machine learning model type and size, along with selecting hyperparameter values, as illustrated by Gauen, K., et al. and Probst, P., et al. . The trained model typically consist of millions of parameters, and are generalizable to new test spectra.