Issue

Vol. 17 (2025)

Artificial Intelligence-Powered Materials Science

https://doi.org/10.1007/s40820-024-01634-8
Xiaopeng Bai
Department of Mechanical Engineering, The University of Hong Kong, Hong Kong 999077, People’s Republic of China
Xingcai Zhang
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA

Corresponding Author: Xingcai Zhang

xingcai@stanford.edu

Nano-Micro Letters, Vol. 17 (2025), Article Number: 135

Abstract

The advancement of materials has played a pivotal role in the advancement of human civilization, and the emergence of artificial intelligence (AI)-empowered materials science heralds a new era with substantial potential to tackle the escalating challenges related to energy, environment, and biomedical concerns in a sustainable manner. The exploration and development of sustainable materials are poised to assume a critical role in attaining technologically advanced solutions that are environmentally friendly, energy-efficient, and conducive to human well-being. This review provides a comprehensive overview of the current scholarly progress in artificial intelligence-powered materials science and its cutting-edge applications. We anticipate that AI technology will be extensively utilized in material research and development, thereby expediting the growth and implementation of novel materials. AI will serve as a catalyst for materials innovation, and in turn, advancements in materials innovation will further enhance the capabilities of AI and AI-powered materials science. Through the synergistic collaboration between AI and materials science, we stand to realize a future propelled by advanced AI-powered materials.


Highlights:
1 A detailed exploration is provided of how artificial intelligence (AI) and machine learning techniques are applied across various aspects of materials science.
2 Major challenges in AI-driven materials science are evaluated.
3 Novel case studies are incorporated, demonstrating their impact on accelerating material development and discovery.

Keywords

Artificial intelligence Machine learning Sustainable materials Data-driven Materials innovation
Bai, Xiaopeng, and Xingcai Zhang. 2025. “Artificial Intelligence-Powered Materials Science”. Nano-Micro Letters 17 (February):135. https://doi.org/10.1007/s40820-024-01634-8.
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