## 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
This open-source tool has greatly lowered the application threshold of MLP and promoted the field standardization process. In view of the limitations of traditional empirical force fields, [PERSON] et al. [57] proposed the atomic cluster Extension (ACE) framework in 2021, which uses symmetric polynomials to construct linear force fields, and achieves an order of magnitude higher accuracy than traditional methods in scenarios such as high temperature dynamics and bond ionization energy. ACE's universal design has quickly become the new benchmark for modeling complex chemical systems. In recent years, MLPS have begun to develop deeper into specific material systems. In 2024, [PERSON] et al. [58] used DeepMD to reveal the atomic mechanism of nucleation and defect formation during the growth of carbon nanotubes, achieving the leap from DFT accuracy to classical molecular dynamics speed. Based on the ACE framework, [PERSON] et al. [59] developed a special potential function for Au\({}_{\text{n}}\)(SCH)\({}_{\text{m}}\), which provided a new research tool for nanocatalizations due to its long-range simulation capability. These breakthroughs demonstrate that the accuracy of MLP force fields is rapidly approaching the precision of traditional DFT calculations, paving the way for large-scale simulations of multi-atom systems and uncovering previously unknown mechanisms. The continuous integration of deep learning and MD heralds a new era in materials science, empowering researchers to explore uncharted territories in chemical and physical phenomena. In 2024, [PERSON] et al. [58] reported the research on simulating the growth process of carbon nanotubes (CNTS) using machine learning force fields (MLFF), as shown in Figure 9, and developed a new type of MLFF--DeepCNT-22. To construct the DeepCNT-22 dataset, researchers adopted multiple methods to generate the initial structures, including molecular dynamics (MD) simulations driven by density functional tight binding (DFTB), random perturbations of the structures, and carbon allotrope structures extracted from the GAP-20 database. Based on the initial data set, the study further introduces a variant of the active learning strategy to refine the data. By training a set of MLFF models, researchers used predictive force bias in the process of simulating the growth of single-walled carbon nanotubes (SWCNT) to identify structures that emerged during the growth process but were not adequately represented by the training data. With the aid of this MLFF system, researchers carried out molecular dynamics simulations of the growth mechanism of CNT and deeply explored the entire process from initial nucleation to tube growth, as well as defect formation and repair. The research finds that the growth of CNT is not a continuous and stable spiral pattern, but an intermittent process affected by the highly dynamic behavior and configuration entropy of the tube-catalyst interface. Furthermore, the research revealed the random formation of defects during growth and their self-healing mechanism, and analyzed the influence of factors such as growth rate and temperature on defect behavior. Overall, this work not only provides a more detailed atomic-scale understanding of the growth process of CNT, but also offers a theoretical basis and new ideas for regulating and optimizing the CNT preparation process, and provides a reference paradigm for the simulation research of related material systems in the future.