## 3 AI Transforms the Development of Computational Materials Science
### The Model Form That Combines Materials Science and AI
#### 3.3.4 The Differences and Connections Among the Three Models  
These three types of models play significant roles, respectively, in macroscopic performance prediction, microscopic behavior modeling, and multi-scale structure-performance correlation, constituting the key technical system of intelligent material design. Firstly, the data-driven prediction model integrates experimental data and first-principles calculation results to establish the mapping relationship between material composition, structure and physical properties, achieving high-throughput screening and rapid performance evaluation. The core lies in reasonable feature engineering, which usually relies on the understanding of the relationship between the structure and properties of materials by domain experts. Therefore, the accuracy and interpretability of the model are more dependent on the quality and representativeness of the input variables.  
Secondly, the Machine Learning Potential Function (MLIP) is mainly used to simulate the interaction potential energy surface between atoms in material systems. Compared with the traditional classical potential function, MLIPs can significantly expand the spatial and temporal scales of molecular dynamics simulation on the basis of approaching the precision of quantum mechanics, achieving the modeling capability of systems with millions of atoms or even larger. This method is applicable to the study of microscopic dynamic processes such as phase transition, diffusion, and defect evolution, making up for the deficiencies of first-principles calculations in terms of computational scale and efficiency. It is a key link connecting atomic scale and macroscopic behavior.  
Thirdly, the material modeling method based on Graph Neural Networks has developed rapidly in recent years. GNN naturally represents atoms (as nodes) and the chemical bonds between them (as edges) in a graph structure, and can embed physical constraints such as bond length, bond Angle and symmetry, thereby enhancing the physical consistency and generalization ability of the model. GNN performs exceptionally well in dealing with complex material systems, such as multi-component alloys, polymer materials, and interface structures. It can automatically learn descriptors with clear physical and chemical significance for predicting the multi-scale properties of materials. However, GNN models usually require a large amount of high-quality training data, and the computational costs of the training and inference processes are relatively high.  
Overall, these three types of machine learning models are not in a substitutive relationship with each other, but rather constitute a collaborative system for intelligent material modeling. Data-driven models are good at macroscopic performance prediction and are suitable for the material screening and optimization process. MLIPs focuses on microscopic atomic dynamics simulation, providing theoretical support for a deeper understanding of material behavior. GNN takes structural diagram learning as the core and attempts to establish an efficient and natural bridging mechanism between the microstructure and macroscopic properties of materials.