Topic: Artificial Intelligence for Metallic Materials Design and Development

Artificial intelligence is creating new opportunities to accelerate materials discovery, design, and development by revealing complex relationships among composition, processing, structure, and properties. This Special Topic aims to showcase advances in integrating AI with materials science, computational modeling, and experimentation, promoting both methodological innovation and practical applications.

We welcome original research and review articles. Topics of interest include, but are not limited to:

-  Machine learning for materials property prediction, characterization, and performance assessment;

-  Inverse design and multi-objective optimization of materials compositions, structures, and processing routes;

-  Large language models, generative AI, and AI agents for materials research;

-  Physics-informed learning, multiscale modeling, and integration of AI with computational materials science;

-  Materials databases, knowledge extraction, and fusion of experimental and computational data;

-  Active learning, high-throughput screening, autonomous experimentation, and intelligent manufacturing.

The scope encompasses diverse materials systems and both structural and functional applications. Studies connecting AI methods with scientific understanding, experimental validation, or manufacturing applications are particularly encouraged. Related emerging approaches and interdisciplinary contributions that advance AI-assisted materials design and development are also welcome.