Due to the inherent sequential processing capability of RNNs, no additional masking or positional encoding was necessary. The molecule decoder, comprising 42M parameters total, was optimized by minimizing the sparse categorical cross-entropy loss function. We considered two training schemes, jointly training Spectro and j-IR-vis end-to-end, and utilizing fixed embedded representations from pretrained models. Our code is available at .