We compared the classification accuracies between the CLICS-pretrained model and the supervised model using ALIGNN from scratch. For the CLICS-pretrained model, we only trained the weights of the projection head from scratch and the remained weights were frozen. For the scratch model, all the weights of ALIGNN and the projection head were trained. The output dimensions of the projection heads were both set to the number of crystal structures. For a fair comparison, we varied the batch size and the learning rate and selected the best results for the scratch model, while the batch size and the learning rate were fixed for CLICS-pretrained model predetermined on an imbalanced task with 50 percent training data among all the data. Figure plots the top-1 and 5 classification accuracies against the number of shot. For 1, 2, and 4-shot tasks, the CLICS-pretrained model classified as top-1 the crystal structures accurately with more than twice the chance compared to the scratch model. Figure plots the classification accuracies for the imbalanced tasks, against the ratio of training data in the total number of data. As the same as the few-shot tasks, the CLICS-pretrained model outperformed the scratch model for all the ratios. A detailed analysis of how accurately each crystal structure was classified is discussed in the supplementary information.