## 3 AI Transforms the Development of Computational Materials Science
### The Model Form That Combines Materials Science and AI
#### 3.3.3 Graph Neural Network Based on Molecular Structure
Notably, its simulation of C20 fullerene dynamics reduced computational time by 50% while maintaining quantum-mechanical precision, validating its efficiency in complex molecular systems. By integrating spatial awareness with adaptive feature learning, SchNet established a new paradigm for data-driven materials modeling, bridging the gap between computational efficiency and quantum-level accuracy. The introduction of Transformer architecture and attention mechanisms in 2018 revolutionized feature extraction in neural networks, aligning particularly well with the localized chemical environments inherent to molecular systems. This breakthrough propelled graph attention mechanisms to the forefront of GNN development. In 2019, [PERSON] et al. [63] reported a Graph Transformer Networks, which pioneered the integration of Transformer self-attention into GNNs, significantly enhancing graph representation capabilities by capturing both short-range atomic interactions and long-range structural dependencies. The model demonstrated superior performance in predicting electronic, mechanical, and thermal properties across diverse material systems, achieving 15-25% higher accuracy than conventional GNNs in alloy phase stability predictions. Building on this foundation, [PERSON] et al. developed GATGNN [64] in 2020, a ground-breaking deep graph network incorporating dual-scale attention for crystal property prediction. The architecture combines local attention layers and global attention layers. Local attention layers focus on atomic-scale environments, such as coordination numbers and bond angles, to precisely capture local information around atoms. Global attention layers perform weighted aggregation of atomic environment vectors to construct crystal-level representations, providing a comprehensive reflection of the overall crystal structure. This hierarchical approach addressed the limitations of traditional GNNs that overemphasized local interactions, enabling GATGNN to model complex atomic relationships in polycrystalline systems and metastable phases with 20-30% improved prediction consistency. In benchmark tests on perovskite stability and metal-organic framework porosity, GATGNN outperformed baseline models by reducing mean absolute errors to <0.08 eV and <1.5 A\({}^{3}\), respectively. The framework's ability to balance local precision with global context has made it particularly effective for multicomponent systems where cooperative effects dominate material behavior, such as high-entropy alloys and layered heterostructures. In addition to the material prediction of inorganic materials, GNN plays an important role in the large-scale screening and structural prediction of drug molecules. In 2021, [PERSON] et al. [65] used a graph recurrent NN model for drug molecular structure generation,namely MGRNN (Molecular Graph Recurrent Neural Network). The model adopts an iterative way of generating nodes and edges and optimizes the training process through a breadth. It has good performance on specific datasets such as ZINC250k, ChEMBL, and MOSES with databases at the million-level. GNNs can not only model graph nodes at the atomic level, but also take atomic aggregates as graph nodes for learning and prediction. It is not only applicable to the prediction of single molecular structure, but also has been applied to large molecular systems such as crystal polymers. In 2024, [PERSON] and [PERSON] [66] used atomic-level GNNs to predict the properties of network materials and proposed a new material representation method. By transmitting information on course-grained crystal graphs, it aims to overcome the limitations of existing methods. The neural network prediction performance and energy efficiency of different material representation methods were compared, including models based on composition and crystal structure. The results show that the coarse-grained crystal Graph Neural Network reduces the computational cost while maintaining accuracy and becomes a strong competitor of atomic-level algorithms. In 2023, [PERSON] et al.