For the prediction of 1 H-NMR and 13 C-NMR, we employed a transfer learning model as introduced earlier in . Such models help to transfer the integrity of learned chemical representation from GNN models to new molecular properties and datasets. The transfer learning architecture used is a simple feed forward neural network (made up of one layer of 128 nodes, followed by "tanh" activation, followed by another linear layer of 128 nodes), that intakes energy-trained embeddings (from SchNet) as inputs to transfer learn to other properties such as 1 H-NMR, and 13 C-NMR. This follows very closely to the transfer learning architecture used in previous works, only here the activation function has been replaced from linear or "Relu", to "tanh."