In summary, we developed direction-based crystal graph convolutional neural network to solve IS2RE task. By comparing with other methods, we found that crystal graph representations are critical in predicting formation energies of the initial structures. Too structure-sensitive representations resulted in significantly different graphs for the initial and relaxed structures (low graph similarity), while too structure-insensitive representations made their graphs nearly identical (high graph similarity). The direction-based representation is neither too structure-sensitive nor -insensitive, making it best suited to IS2RE tasks. For the IS2RE task, D-CGCNN indeed achieved 0.299 eV/atom of test set MAE, the lowest MAE among other graph-based approaches (0.419 eV/atom for CGCNN-HD, 0.345 eV/atom for Roost and 0.315 eV/atom for Wren). Furthermore, D-CGCNN achieved both high success ratio and discoverability (72% and 78%, respectively), two metrics of the high-throughput screening performance. These results highlight that D-CGCNN could accelerate the discovery of stable candidates by using the initial structures as inputs.