AI-Driven Materials Design, Discovery and Manufacturing

Dear Colleagues,

Artificial intelligence (AI) is increasingly reshaping how materials are designed, discovered, and manufactured, enabling a shift from trial-and-error approaches toward predictive, data-driven workflows. This Topic focuses on recent advances in AI-enabled materials engineering across multiple length scales, from nanoscale architectures to structural systems.

The covered research scope of this Topic spans machine learning-based design, performance forecasting and structural optimization of various advanced materials, including high-performance alloys, composite materials and multifunctional functional materials, as well as core theoretical frameworks, data construction methods and analytical tools for materials informatics. It also embraces interdisciplinary research that combines AI algorithms, big data mining and digital simulation technologies with precision processing and intelligent manufacturing techniques. Special attention is given to high-performance multifunctional materials and environmentally sustainable materials tailored to industrial practical needs. We particularly encourage studies that integrate computational simulation, theoretical analysis and standardized experimental verification, which can effectively bridge the gap between basic theoretical research and practical engineering applications of new materials.

Applications spanning biomedical engineering, aerospace, energy, and defence systems are of strong interest. The aim is to capture work that not only advances fundamental understanding but also demonstrates clear pathways toward scalable, high-performance, and application-ready material solutions.

Dr. Azadeh Mirabedini
Dr. Ze-Feng Gao
Topic Editors