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

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Authors: Ying Cao, Fu Hong, Jian Lu, Yuejiao Chen, Titao Jing, Xi Fan, Bingang Xu
Cited by: 14
References: 185

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.

Fields of Study & Subjects

Machine Learning in Materials Science Catalysis and Oxidation Reactions Material Selection and Properties

Publication details

Journal / source
Nano-Micro Letters
Publisher
Springer Science and Business Media LLC
Volume
18
Issue
1
Pages
109–109
Publication date
10 January 2026
Language
EN