We began our benchmark studies with the MPcrystals-2018.6.1 dataset provided by MEG-Net, which includes DFT-computed energies and band gaps for 69,640 crystals from the Materials Project. We trained on 60,000 crystal structures from this dataset, achieving a mean absolute error (MAE) of 29.5 ± 0. Neverthe-less, it is worth mentioning that both training and inference steps involve a significant computational overhead when switching from PiNet to PiNet2-P5 (see Fig. in the Supporting Information) and the most cost-effective improvement is led by PiNet2-P3 instead. Therefore, in the following sections, we will focus on the comparison between PiNet and PiNet2-P3.