## 1 The Evolution of Sports: A Synergy of Historical Development and Technological Innovation
Its excellent torsional stiffness and interwoven structure can effectively disperse impact energy and suppress crack propagation under dynamic loads, thus, becoming an ideal choice for sports equipment such as tennis rackets. While multi-directional braiding extends the reinforcing phase to the Z-axis direction through 3D interweaving, although it significantly enhances the interlaminar shear strength (up to several times that of the UD) and is suitable for complex geometric structures such as aerospace connectors, it also leads to a significant increase in manufacturing costs and cycles. Similarly, the mechanical properties of metallic alloys are also closely related to their fine structures [20]. The differences between the two typical crystal structures, face-centered cubic (fcc) and body-centered cubic (bcc), are particularly significant: The atoms of the fcc structure are arranged at the corners and face-centered positions of the cube, with 12 slip systems, demonstrating excellent ductility and low-temperature toughness and, thus, are widely used in fields such as automotive panels, cryogenic storage tanks, and biomedical implants. The atoms of the bcc structure are located at the corner points and the center of the cube. Although it has higher strength and wear resistance, its low-temperature ductility is relatively poor, making it more suitable for applications such as heavy-duty bearings, armor plates, and high-strength fasteners. These examples fully demonstrate that the precise regulation of the microstructure of materials is the key to achieving performance optimization. Precise control of material properties across scales is the core scientific challenge currently faced. The macroscopic properties of materials are influenced by the multi-scale synergy of electronic structure (quantum scale), crystal structure (atomic scale), and microstructure (mesoscopic scale). To achieve precise regulation of material properties, it is necessary to establish a full-chain control capability ranging from electronic structure calculation, crystal defect engineering to microstructure design, and ensure the mutual compatibility and collaborative optimization of mechanisms at various scales. However, this process faces fundamental difficulties. From a physical nature perspective, the complex electronic structure of the material system makes precise description face theoretical bottlenecks. In most cases, the Schrodinger equation lacks analytical solutions, and the computational complexity of solving it by numerical methods increases exponentially with the size of the system, far exceeding the processing capacity of existing computing resources. Researchers must adopt various approximation methods to handle behaviors at different scales. Although this partially alleviates the problem of computational efficiency, it inevitably introduces the loss of accuracy, and this error will continuously accumulate and amplify during the multi-scale simulation process. With the expansion of the scale of the research system, the computational complexity and time cost increase sharply and nonlinearly, which poses huge challenges for traditional computational methods when solving practical problems. It is worth noting that artificial intelligence technology provides a revolutionary new idea for solving this predicament: through the nonlinear mapping ability of deep neural networks, cross-scale correlation models can be efficiently established. Machine learning algorithms can extract feature patterns from massive data, significantly reducing the computational complexity of solving complex equations. This data-driven research paradigm not only holds the potential to break through the trade-off dilemma between accuracy and efficiency in traditional methods, but also enables truly precise cross-scale regulation, opening up a brand-new technical path for material research and development and promoting the transformation of materials science towards an intelligent design paradigm.