Another class of ML models is deep learning models, such as graph-based neural networks (GNNs). Different from CML models, GNNs are tailored to manage graph-structured data, where each graph comprises nodes and edges. Previous studies have demonstrated the superior abilities of GNNs in extracting and propagating information within graph structures, rendering them suitable for predicting properties of graph-representable materials such as crystals and molecules, where atoms serve as nodes and interactions or bonds represent edges. For example, Cheng et al. designed a geometric-information-enhanced crystal graph neural network (GeoCGNN) for crystalline materials and demonstrated superior performance for predicting formation energy compared to other GNN based models. In addition, Gu et al. integrated positive-unlabeled learning, domain-specific learning, and transfer learning to develop a GNN model for predicting the synthesizability of all types of perovskites, significantly outperforming other traditional methods. So far, a wide range of ML models, encompassing both CML and GNN-based models, have been devised to uncover the correlations between compositions and properties of perovskite materials. To the best of our knowledge, there remains a gap in literature for a comprehensive and precise evaluation of the accuracy and efficiency of various ML models in predicting perovskite properties.