Due to the complexity of molecular elucidation and its extensive application in synthetic chemistry characterization, numerous attempts have been made to automate or assist scientists through algorithms, models, and data-driven processes. Early efforts include GENIUS , which employs evolutionary algorithms to generate candidate structures and predict their 13 C spectra using a surrogate neural network, demonstrating the potential of combining traditional chemical knowledge with Figure : The diagrams of Spectro and j-IR-vis. statistical algorithms. Other examples include logical algorithms that generate structures consistent with 1D and 2D NMR data, ranking candidates based on the alignment of predicted versus experimental chemical shifts . SESAMI interprets mass spectral fragmentation and IR absorption to build partial structures and suggest complete molecules. Some approaches also incorporate techniques like Raman and ion mobility spectrometry to provide additional structural information. Recent machine learning (ML) based approaches have been proposed as well. For example, ML models have been developed to classify IR spectra based on functional groups and to predict IR spectra from molecular structures . Given the importance of NMR in molecular elucidation, ML-based models have also been introduced to identify molecular substructures from NMR spectra or to match potential molecules with their spectra . Additionally, algorithms have been designed to emulate a spectroscopist's approach to molecular elucidation, identifying small structural fragments from IR and NMR data and then connecting these fragments to build a complete structure .