## 3 AI Transforms the Development of Computational Materials Science
### The Model Form That Combines Materials Science and AI
#### 3.3.1 Machine Learning and Neural Network Data-Driven Models in Materials Science  
In 2019, [PERSON] et al. [27] reviewed the application and development of methods from density functional theory (DFT) to machine learning (ML) in materials science, as shown in Figure 5. With the advancement of experimental and computational methods, a vast amount of data has been accumulated in the field of materials. How to effectively utilize this data to guide the design of new materials has become an important challenge. The article first reviews the important position of DFT in the calculation of material properties, emphasizing its high accuracy and wide application range. Subsequently, the advantages of high-throughput (HT) computing in generating structural and property data were intro \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Form** & **Content** & **Features** & **Disadvantages** \\ \hline \multirow{4}{*}{Data-driven Machine Learning Prediction Model [43; 44]} & Trains machine learning models using large-scale experimental and computational data to establish a mapping relationship between material properties and structure. & Fast prediction of material properties. Efficient handling of complex, multi-dimensional data to discover potential patterns. & Strong dependency on high-quality data. The availability and balance of data directly affect model performance. \\ \hline \multirow{4}{*}{Machine Learning Potentials [45]} & Construct potential functions (Machine Learning Potentials, MLPs) through machine learning techniques to replace traditional quantum mechanical methods for simulating atomic interactions. & This method is suitable for large-scale molecular dynamics simulations involving electron transfer and chemical bond breaking, applicable to studying reaction mechanisms and dynamic evolution in material systems at the thousand-to ten-thousand-atom scale. \\ \hline \multirow{4}{*}{Graph Neural Network-Based Material Modeling [41]} & Graph Neural Networks (GNNs) directly model the material’s molecular or crystal structure (usually represented as graphs) to predict the chemical properties and physical performance of materials. & Incorporates molecular information from material structures into the prediction model, helping to accurately describe complex chemical bonds and interactions. Compared to traditional neural networks, it offers better physical meaning and interpretability. & High computer resource requirements for building and optimizing graphics \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Types and methods of the AI models. duced, providing a solid data foundation for subsequent machine learning modeling. In the fundamental principles of machine learning prediction, the key lies in constructing a mathematical model capable of learning the relationship between the structure and properties of materials. Firstly, through feature engineering, the material structure information is transformed into numerical features that can be used for modeling, such as atomic type, bond length, charge distribution, band gap, etc. Then, appropriate algorithms (such as Support Vector Machine, Random Forest, Neural Network, etc.) are selected to train the existing data and establish the mapping relationship between structure and property. For data with known properties, supervised learning is adopted for model optimization; in unsupervised learning, the inherent patterns of data are discovered through methods such as clustering or dimensionality reduction, which is applicable to material classification and potential performance discovery. The trained model can be used to quickly predict the properties of new materials, greatly reducing the reliance on first-principles calculations and significantly improving the efficiency of screening and design. Especially when dealing with high-dimensional and complex material data, machine learning demonstrates powerful processing capabilities, providing a brand-new technical path for accelerating material discovery.