## 3 AI Transforms the Development of Computational Materials Science
### The Model Form That Combines Materials Science and AI
#### 3.3.2 Machine Learning Potentials in Materials Science  
Classical Molecular Dynamics (CMD) and Ab Initio Molecular Dynamics (AIMD) [51] are two widely used simulation methods for modeling molecular motion. These simulations explore how molecules \"climb\" the Potential Energy Surface (PES), as shown in Figure 8, which represents the total energy of a molecular system as a function of the relative positions of its nuclei. CMD relies on Newtonian mechanics with relatively large time steps and lower computational complexity, enabling fast simulations of macromolecular dynamics at the millisecond scale, such as protein folding. However, CMD lacks the precision needed for electronic behavior analysis. In contrast, AIMD utilizes first-principles calculations like Density Functional Theory (DFT) to simulate electronic structures with high accuracy. While AIMD can model detailed chemical reaction mechanisms, it requires intensive computations and short time steps, making it less suitable for large-scale systems or long-term simulations. The complementary strengths and limitations of CMD and AIMD highlight the need for a simulation approach that balances computational speed and accuracy. Machine Learning Potentials (MLPs) address this need by leveraging machine learning techniques to construct high-precision, low-cost potential energy surface models. MLPs utilize neural networks and deep learning to capture the local chemical environment of atoms, accurately fitting the relationships between atomic forces and energies. This approach reduces computational complexity while enhancing the generalization capability for complex systems, thus, expanding the applicability of molecular simulations. In 2007, [PERSON] and [PERSON] [53] pioneered the Neural Network Potential Function (MLP), which successfully simulated the interaction of CO/Ni surfaces and H2/Si surfaces by training neural networks on first-principles data. This work not only validates the precision and efficiency of MLP, but also introduces two core concepts of local environment approximation and symmetry descriptor, which lays a methodological foundation for further research. Under this theoretical framework, in 2010, [PERSON] et al. [54] developed the Gaussian Approximation Potential (GAP), which for the first time explicitly decomposed the interatomic interaction into multibody terms: the distance dependence relationship was fit through the two-body descriptor, and the kernel function summary and normalization technology was innovatively adopted to deal with the three-body and higher order terms, so as to achieve robust regression of atomic arrangement changes. To further improve the ability to describe the behavior of complex materials, in 2015, [PERSON] et al. [55] proposed developed the spectral Neighborhood Analysis Potential (SNAP) model: assuming a linear relationship between the atomic energy and the bispectrality component of the local neighborhood density, the weighted least squares regression  
Figure 8: Machine Learning Potential functions (MLPs) learn potential energies from quantum mechanical calculations or experimental data, enabling high-precision molecular dynamics simulations (e.g., material molecule formation reactions) at low computational costs [52]. Copyright 2020 American Chemical Society. is used to fit large-scale quantum mechanical data. SNAP has demonstrated excellent performance in predicting screw dislocation migration for BCC tantalum, revealing for the first time the microscopic mechanism of material plastic deformation under shear stress. The deep integration of deep learning and molecular dynamics was a breakthrough in 2018, [PERSON] et al. [56] developed the DeePMD-kit model, which enables end-to-end training through TensorFlow, with automated descriptor calculations and force field evaluation modules that bring computational efficiency to a level of accuracy comparable to DFT, while supporting million-atom-scale simulations.