## 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  
GNNs exhibit an intrinsic compatibility with molecular structure analysis, making them uniquely suited for predicting material properties. This natural alignment stems from the inherent similarity between molecular configurations and graph representations. Atoms (e.g., carbon, hydrogen, oxygen) are encoded as nodes with features such as electronegativity and valence electrons, while chemical bonds (single, double, or triple) form edges characterized by bond type, strength, and geometric parameters like bond length, as shown in Figure 10. This graph-based framework mirrors the structural essence of molecules, allowing GNNs to model atomic interactions with exceptional precision. GNNs leverage message-passing mechanisms to aggregate neighborhood information, emulating how molecular properties emerge from atomic interactions. They capture both local molecular features--such as the reactivity of functional groups (e.g., hydroxyl radicals)--and global structural patterns, including ring systems or polymer chains that influence solubility. Through hierarchical aggregation across network layers, GNNs holistically characterize molecular behavior. Critically, their permutation invariance ensures predictions remain unaffected by atom ordering, inherently preserving molecular symmetry without requiring data augmentation. This structural fidelity, combined with computational efficiency, enables GNNs to outperform traditional methods in property prediction tasks, particularly for complex molecular systems where quantum simulations are computationally prohibitive. Figure 10: The material modeling based on a Graph Neural Network has the function of intrinsic molecular structure information. (**a**) The molecular segmentation method is used to divide molecules into different clusters, with atoms as nodes and chemical bonds as edges, and convert them into graph data. (**b**) Atomic nodes conduct information transmission within molecular categories, learning the characteristics within functional groups to form a comprehensive understanding of the global structural information. (**c**) Through the information transmission of molecular segmentation and mask quantization, the molecular structure information that is prone to loss is optimized to improve the interpretability of the model [60]. Figure 9: Machine learning force fields have demonstrated outstanding accuracy in simulating the growth reaction process of carbon nanotubes at the atomic scale. They not only enable nanosecond-level efficient calculations but also accurately predict the chiral structural characteristics of carbon nanotubes [58]. The development of GNN architecture has been driven by advancements in modeling capabilities. In 2017, [PERSON] et al. reported a Message-Passing Neural Networks [61] (MPNNs), which gained prominence in material property prediction through their message-passing framework, early models fell short in capturing multi-body interactions such as dihedral angles. In 2018, [PERSON] et al. introduced SchNet [62], a deep learning model specifically designed for molecular and material property prediction using 3D Graph Neural Networks (3D-GNNs). It uses continuous filter convolution (CFCONV) layers to process atomic space coordinates, enabling accurate modeling of 3D atomic interactions that significantly enhance local interaction characterization while efficiently resolving many-body atomic effects, enabling accurate modeling of molecular and crystal structural features. SchNet has demonstrated excellent performance in predicting various material properties, especially formation energy. The model's ability to generate local chemical potential revealed enhanced interpretability for chemical concepts such as bond saturation and aromaticity, surpassing the Deep Tensor Neural Network in these tasks. In MD applications, SchNet achieved state-of-the-art results on the MD17 dataset, accurately predicting combined energy and force profiles with 30% faster convergence than the GAP-based GDML framework.