Journal Article

Advances in machine learning- and artificial intelligence-assisted material design of steels

Gao, Junheng, Wang, Shuize, Wu, Honghui, Shang, Chunlei, Zhou, Xiaoye, Gao, Zhijun, Wu, Guilin, Wang, Feiyang, Pan, Guangfei, Mao, Xinping

2023

Overview

With the rapid development of artificial intelligence technology and increasing material data, machine learning- and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science. Machine learning methods, based on an interdisciplinary discipline between computer science, statistics and material science, are good at discovering correlations between numerous data points. Compared with the traditional physical modeling method in material science, the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials. This review starts with data preprocessing and the introduction of different machine learning models, including algorithm selection and model evaluation. Then, some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition, structure, processing, and performance. The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed. Finally, the applicability and limitations of machine learning in the material field are summarized, and future directions and prospects are discussed.

• Publisher: University of Science and Technology Beijing,Springer Nature B.V,Yangjiang Branch,Guangdong Laboratory for Materials Science and Technology(Yangjiang Advanced Alloys Laboratory),Yangjiang 529500,China%Guangdong Province Key Laboratory of Durability for Marine Civil Engineering,School of Civil Engineering,Shenzhen University,Shenzhen 518060,China,Beijing Advanced Innovation Center for Materials Genome Engineering,University of Science and Technology Beijing,Beijing 100083,China%Beijing Advanced Innovation Center for Materials Genome Engineering,University of Science and Technology Beijing,Beijing 100083,China
• Subject: Algorithms / Artificial intelligence / Ceramics / Characterization and Evaluation of Materials / Chemistry and Materials Science / Composites / Composition / Corrosion and Coatings / Data points / Design defects / Glass / Inverse design / Invited Review / Machine learning / Materials Science / Metallic Materials / Natural Materials / R&D / Research & development / Steel / Surfaces and Interfaces / Thin Films / Tribology