Encoding spectra data in latent space retains functional group prediction performance. Given the success of our MLP model in predicting functional groups using complete standardized spectra, we wished to investigate the ability of an autoencoder to reduce the spectra into a latent space. We trained a simple linear model for encoding the IR and MS spectra into a 256-length vector and decoding this vector back to the original spectra used to create the vector (see Figure ). The 256-length vector was used to train a second network for multi-task functional group prediction. For individual functional groups, the autoencoder model performs similar to that of the original MLP model (F1 scores are given in Tables and) The molecular performance of the autoencoder model is similar to that of the original MLP model (Figure ) as the MPR for the autoencoder model is 62.6% and the MF1 score is 0.905 as compared to 65.2% and 0.912 for the original model (Tables and). This reveals that the original spectra contain redundant features that relate IR and mass spectra. We plan to explore the use of this latent space for inverse design of molecules with combined spectral properties in future works.