## 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
(**b**) systematically conducts feature engineering processing on the molecular structure characteristics. (**c**) The unsupervised learning method is adopted to preprocess the material data. (**d**) conducts training and performance evaluation through a machine learning model [46]. Copyright 2024 American Chemical Society. Due to the high cost of experiments on material data, model training for small-scale databases requires the development of efficient algorithms with strong generalization capabilities. Although the traditional machine learning method has been widely used in material computation, it is often difficult to fully analyze the high-dimensional complex laws of molecular systems due to the relatively simple structure of the model. In 2022, [PERSON] et al. [48] research shows that these traditional methods have significant limitations in obtaining accurate target-specific data. Among them, kernel-based methods, such as KRR, improve predictive performance to some extent by combining nuclear techniques with regression models. In recent years, the breakthrough of attention mechanism provides a new way to solve this problem. The technology automatically focuses on key data features and exhibits excellent generalization capabilities, which are highly compatible with the coexistence of local and global interactions in the material molecule. For example, in 2022 [PERSON] et al. [49] developed CrabNet model, which innovatively applies the self-attention mechanism in natural language processing to materials science and effectively learns element embedding vectors. At the same time, the mat2 vec model based on word embedding technology realizes high-quality compound prediction by capturing the context relationship between elements. These models have been rigorously tested and have demonstrated excellent predictive performance. On this basis, in 2023, [PERSON] et al. [50] further proposed an innovative fusion strategy model, which successfully achieved accurate modeling of complex molecular interactions by deeply integrating the global feature extractor (LSTM/GRU), the local feature extractor (DCNN) and the novel CECV (Coupled Enhanced Context Vectorization) feedback mechanism. This breakthrough not only overcomes the limitations of traditional rule-based systems, but also establishes a new research paradigm for efficient learning of small sample material data. Figure 7: **(a)** The process of collecting data from images, model training to predicting new alloy compositions. **(b)** The process of training the sampling model is demonstrated from the microscopic experimental image data [47]. **(c)** A schematic diagram of the training of the machine learning model used, which consists of three sub-models, namely the encoder model, the decoder model and the regression model. The output of the regression model is basically the concentration of the alloy. During the training process, the image is both the input and the label, with the aim of minimizing the difference between the input and output images. And the latent space is further dimensionally reduced through kernel principal component analysis. The fine structure of the hidden layer is not shown. The hidden layers include batch normalization layers, convolutional layers, dropout layers, activation layers, etc.