The first branch handles the edge string, which is represented by a noisy one-hot encoded vector similar to the work of Shen et al. The second branch processes the node and topology string using token embeddings. This approach is particularly beneficial for the nondifferentiable portion of the input, as token embeddings are proficient at capturing nuanced semantic relationships among different tokens. An integral component of this architecture is the softmax self-attention mechanism applied to the outputs of both LSTM branches. By incorporating self-attention, the model can focus on the most relevant parts of the input sequence for the regression task at hand. More importantly, this augmentation enhances the model's physical interpretability because the attention weights, when applied during inference, provide insights into the importance of different tokens within our MOF strings and their contributions to the model's predictions. Finally, the context vectors obtained from both LSTM branches are then concatenated and passed through an MLP to obtain the final property prediction. Additional details on the the justification of our model architecture, model hyperparameters, and training can be found in Supplementary Note 3.