Guangfei Pan, Feiyang Wang, Chunlei Shang, Honghui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao, Xiaoye Zhou, and Xinping Mao, Advances in machine learning- and artificial intelligence-assisted material design of steels, Int. J. Miner. Metall. Mater.,(2023).
Cite this article as:
Guangfei Pan, Feiyang Wang, Chunlei Shang, Honghui Wu, Guilin Wu, Junheng Gao, Shuize Wang, Zhijun Gao, Xiaoye Zhou, and Xinping Mao, Advances in machine learning- and artificial intelligence-assisted material design of steels, Int. J. Miner. Metall. Mater.,(2023).
Invited Review

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

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  • Corresponding authors:

    Honghui Wu    E-mail:

    Shuize Wang    E-mail:

  • Received: 14 October 2022Revised: 9 December 2022Accepted: 29 December 2022Available online: 30 December 2022
  • 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.
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