• Atomistic Line Graph Neural Network (ALIGNN): ALIGNN enhances conventional graph neural networks by incorporating bond angle information from line graphs, thereby improving the model's capability to predict complex material properties with higher accuracy . • Crystal Twins (CT): CT is a self-supervised pre-trained model for crystalline material property prediction, using twin CGCNNs to learn robust representations from large unlabeled datasets, which are then finetuned for specific tasks .