Issue

Vol. 18 (2026)

Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation

https://doi.org/10.1007/s40820-025-01945-4
Ying Cao
Nanotechnology Center, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong 999077, People’s Republic of China
Hong Fu
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong 999077, People’s Republic of China
Jian Lu
Nanotechnology Center, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong 999077, People’s Republic of China
Yuejiao Chen
State Key Laboratory for Powder Metallurgy, Central South University, Changsha 410083, People’s Republic of China
Titao Jing
School of Chemical Engineering and Technology, Sun Yat-Sen University, Zhuhai 519082, People’s Republic of China
Xi Fan
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, People’s Republic of China
Bingang Xu
Nanotechnology Center, School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong 999077, People’s Republic of China

Corresponding Author: Bingang Xu

tcxubg@polyu.edu.hk

Nano-Micro Letters, Vol. 18 (2026), Article Number: 109

Abstract

Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence (AI). AI has successfully been applied for predicting the formability, revealing the properties, and guiding the experimental synthesis of materials. Rapid progress has been made in the integration of increasing database and improved computing power. Though some reviews present the development from their unique aspects, reviews from the view of how AI empowered both discovery of new materials and cognition of existing materials that covers the completed contents with two synergistical aspects are few. Here, the newest development is systematically reviewed in the field of AI empowered materials, reflecting advanced design of the intelligent systems for discovery, synthesis, prediction and validation of materials. First, background and mechanisms are briefed, after which the design for the AI systems with data, machine learning and automated laboratory included is illustrated. Next, strategies are summarized to obtain the AI systems for materials with improved performance which comprehensively cover the aspects from the in-depth cognizance of existing material and the rapid discovery of new materials, and then, the design thought for future AI systems in material science is pointed out. Finally, some perspectives are put forward.


Highlights:
1 A comprehensive review focused on the recent advancement of artificial intelligence (AI) powered materials research from various aspects, including material discovery, synthesis, prediction and validation, is presented.
2 The design strategies for the enhanced performance of AI for materials can be implemented from various procedures for cognizance of existing materials and discovery of novel materials with the data processing, algorithm design and automated laboratory construction included.
3 A broad outlook on the future considerations of the AI systems for material is proposed.

Keywords

Artificial intelligence Material discovery and cognition Design tactics Review and perspective
Cao, Ying, Hong Fu, Jian Lu, Yuejiao Chen, Titao Jing, Xi Fan, and Bingang Xu. 2026. “Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation”. Nano-Micro Letters 18 (January):109. https://doi.org/10.1007/s40820-025-01945-4.
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