Topic: Design, Optimization, and Characterization of the Microstructure of High-Performance Metallic Materials

A material’s microstructure plays a decisive role in determining its properties. The design, optimization, and characterization of microstructures to enhance material performance have therefore long been a central focus in materials research. Over the past few decades, this field has witnessed remarkable advances. For instance, designing and optimizing metal materials with hierarchically heterogeneous microstructures has enabled simultaneous achievements in strength and ductility, thereby helping to overcome the long-standing strength-ductility trade-off. These developments have also contributed to the emergence of heterogeneous structural materials as an important area within materials science. Meanwhile, characterization capabilities have advanced substantially, progressing from the micrometer scale of optical microscopy to the ultra-high-precision nanoscale characterization offered by electron microscopy (such as Atom Probe Tomography, via APT). Furthermore, in recent years, rapidly advancing artificial intelligence technologies—such as deep learning and machine learning—have played an increasingly important role in supporting the design and optimization of metallic microstructures. AI technology has significantly shortened the time and processes involved in screening microstructures, greatly enhancing efficiency in the development of high-performance materials. As demand for higher-performance materials continues to grow, it is crucial to develop advanced materials with superior properties by designing, optimizing, and characterizing their microstructures.

Microstructure design, microstructure characterization, heterogeneous structural materials, structure–property relationships, advanced electron microscopy, atom probe tomography (APT), artificial intelligence, machine learning, data-driven materials science, high-performance