Hyperparameters We use a learning rate of 10 -4 . For the contrastive loss, a temperature (τ) of 0.07 is used following Wu et al. The temperature controls the strength of penalties on the negative samples (i.e. spectra and SMILES embeddings that do not match). We use the early-stopping convergence criterion for the model with a patience of 10 epochs.