## 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
The prediction of molecular properties relies on the molecular information extracted from computational materials science data. By establishing the mapping relationship between the molecular structure property descriptors and the macroscopic molecular properties, and with the help of machine learning model training to obtain the optimal prediction model, efficient prediction and optimization of performance can be achieved. In 2024, [PERSON] et al. [46] conducted a study to explore the application of machine learning (ML) technology in accelerating material discovery, as shown in Figure 6, with a particular focus on the utilization of electronic band structure data. This research is based on the Materials Project database, collecting the electronic band structure data of 63,588 kinds of materials, and conducting classification and cluster analysis of the materials through multiple ML algorithms. Before data modeling, researchers conducted systematic data preprocessing, including feature selection, feature engineering and noise reduction processing, to improve the prediction accuracy and computational efficiency of the model. Subsequently, three unsupervised learning algorithms--K-means, AGNES, and BIRCH--were adopted in the study to conduct cluster analysis on the material data and mine the similarity features among the materials, thereby accelerating the discovery process of new materials. The results show that the clustering method based on the characteristics of electronic energy points outperforms the models that only use category attributes such as band gaps or Fermi levels in terms of performance. Among them, the AGNES algorithm shows the optimal clustering effect in most experiments. This research provides a systematic and effective methodology for analyzing and predicting the electronic properties of materials using  
Figure 5: Data-driven machine learning prediction model principle [27]. machine learning methods, and is expected to play a key role in future material design and target performance-oriented material screening. The second type of data source comes from experimental collection. Through in-depth research on the relationship between the characterization of material properties and macroscopic performance, it has promoted the development of data-based material design methods. In 2021, as shown in Figure 7, [PERSON] et al. [47] reported that a hybrid neural network method integrating variational autoencoder (VAE) and regression model was innovatively proposed to solve the problem of microstructure identification in martensitic steel and successfully achieved the reverse design of up to 20 elemental alloys. This work has significantly broken through the bottlenecks of traditional methods in microstructure identification and component design. Specifically, this method first uses VAE to encode the microstructure image into a 64-dimensional latent space, achieving efficient compression and representation of complex microscopic features. Then, a weighted regression model (with a weight ratio of 100:1) is combined to guide the distribution of the latent space, enabling it to better reflect the latent correlation between the material composition and properties. Finally, with the aid of 2D kernel principal component analysis (2D-kernel PCA), the back mapping from the latent space to the specific material composition is achieved, realizing the reverse design from microstructure to composition. Compared with the traditional trial-and-error methods, this technology has significant advantages: on the premise of using only hundreds of microstructure images as training data, it still achieves high-precision prediction and design capabilities, greatly improving the efficiency of data utilization. This method has not only been successfully applied to the intelligent design of 9-12% Cr martensitic steel but also has good scalability and can be extended to complex multi-component material systems such as high-entropy alloys. Figure 6: (**a**) Establish the database of the target.