The architectures of the generator and predictor were derived from the original Transformer paper . For the generator, the Transformer encoder and decoder consist of 3 layers, 4 heads, and a hidden size of 256. The maximum length of the data representations of MOFs is 128. The model was trained with a batch size of 128 during 50 epochs and AdamW optimizer with a learning rate of 10 -4 . The predictor, which is similar to the encoder of the generator, consists of 4 layers of the Transformer encoder. It was trained with a batch size of 128 during 100 epochs. The AdamW optimizer with a learning rate of 10 -4 and weight decay of 10 -2 was used for the predictor. The learning rate was warmed up during the first 5 % of the total epoch and then linearly decayed to zero for the remaining epochs.