New materials, crucial for economic and technological progress, are prioritized globally with strategies to accelerate their advancement through big data and AI. AI for Materials (AI4Mater) serves as an overall framework for integrating AI into Materials Science and Engineering, which is structured around three main elements: materials data infrastructure, AI4Mater techniques, and applications. This article reviews the development procedure and recent innovations in materials data infrastructure, machine learning in materials, autonomous experiment, intelligent computation, and intelligent manufacture. These efforts aim to foster open access to AI resources and enhance the collective advancement of materials science, ultimately accelerating breakthroughs and elevating the engineering application of new materials in a sustainable manner.

Three-dimensional printing technology provides substantial developmental advantages for silicone rubber, such as enhanced precision, the ability of fabricating intricate structures, and accelerated manufacturing processes. This review first offers an overview of 3D printing technology and its commonly used materials, with a particular emphasis on diverse materials introduced in recent years, especially those suitable for high-performance elastomers such as silicone rubber. It then comprehensively analyzes the characteristics, advantages, and challenges of 3D printed silicone rubber, including its processing adaptability and molding performance. A detailed discussion follows the characterization techniques, including mechanical testing, electron microscopy, thermal analysis, and scattering methods, which are used to evaluate the microstructure, mechanical properties, and thermal stability of the printed materials. Finally, the review explores the application of neutron scattering techniques in studying 3D printed silicone rubber, highlighting how these methods deepen the understanding of the material's microstructure, particularly in terms of polymer chain configuration and molecular dynamics. By integrating the latest advancements, this review aims to serve as a valuable reference for design, optimization, and industrial implementation of 3D printed silicone rubber.

The accurate knowledge of liquid properties for Ti-Al based alloys is of great significance for scientific explorations such as revealing the atomic-scale behaviors in alloy processing and realizing the optimization of manufacture technology. By means of the active learning method based on deep neural network (DNN) and electromagnetic levitation technology (EML), we investigate the microstructure evolution of Ti₄₇Al₄₇Zr₆ alloy, and with a focus on obtaining its liquid state properties. The density, surface tension, viscosity of this liquid alloy and the related self-diffusion coefficient are predicted by the DNN potential. The calculated surface tension exhibits only a deviation of less than 2 % as compared with EML experimental values. The liquid local structure characteristics acquired through Voronoi polyhedron analysis indicate that the fraction of relatively high-coordinated clusters with Al as the central atom displayed an anomalous decrease with the falling of liquid temperature. This effect is attributed to the tendency of these clusters to form icosahedral-like geometries, resulting in an increased fraction of icosahedral-like geometries in liquid alloy.

In aerospace, high strength and toughness titanium alloys (HSTTAs) formed by additive manufacturing (AM) are mostly utilized to create complex shaped structural components. It can fulfill the bespoke design specifications of components, enhance material consumption and production efficiency, and decrease costs and time. As the requirement for safety and stability in structural components rises, damage tolerance performance (DTP) has emerged as the design benchmark for titanium alloys in aviation. This review initially presents the historical evolution of HSTTAs and subsequently discusses related research on the HSTTAs formed by AM. This review covers recent research advancements on the DTP of HSTTAs formed by AM, detailing the deformation behavior, fracture toughness, and fatigue crack propagation characteristics of the alloy. The primary approaches for enhancing the DTP of HSTTAs formed by AM, including process parameter optimization and heat treatment, are examined. Finally, the existing problems on current research and prospective research directions are identified. Investigating the DTP of HSTTAs formed by AM can enhance the overall performance of materials and guarantee structural integrity, while also fostering innovation in AM and propelling technological advancement. And it provides certain reference significance for upgrading AM processes and post-treatment processes, optimizing properties, and developing new high damage tolerant titanium alloys.