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<p>In the exploration of advanced titanium (Ti) alloys, the huge composition and process space make it impossible to fully consider every possibility through the traditional trial-and-error methods. With the development of high-throughput technologies and the improvement of computer data analysis capabilities, machine learning (ML) methods based on big data have opened up a new paradigm for materials science research, accelerating the development of materials such as Ti alloys and achieving a series of breakthroughs. However, there is still a lack of systematic generalizations and summaries about the applications of ML methods and high-throughput technologies in the field of Ti alloys. This review first introduces the common ML workflow in materials science. Then, advances in high-throughput preparation technology and ML in the prediction and design of the microstructure and properties of Ti alloys are examined. Finally, the main challenges are discussed, and future directions for ML and high-throughput technologies in materials science are proposed. It is hoped that this review offers useful insights into the integration of these emerging technologies, providing some guidance for future research and applications of Ti alloys.</p> Graphical abstract <p></p>

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Review: Recent progress in titanium alloys development via machine learning and high-throughput preparation

  • 期刊论文

  • 发表日期：

    2025年9月6日

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  • Feiyang Gao,
  • Weiwen Zhang,
  • Xiaotao Liu,
  • Xuan Luo,
  • Zhi Wang,
  • Ning Li,
  • Lehua Liu

摘要

In the exploration of advanced titanium (Ti) alloys, the huge composition and process space make it impossible to fully consider every possibility through the traditional trial-and-error methods. With the development of high-throughput technologies and the improvement of computer data analysis capabilities, machine learning (ML) methods based on big data have opened up a new paradigm for materials science research, accelerating the development of materials such as Ti alloys and achieving a series of breakthroughs. However, there is still a lack of systematic generalizations and summaries about the applications of ML methods and high-throughput technologies in the field of Ti alloys. This review first introduces the common ML workflow in materials science. Then, advances in high-throughput preparation technology and ML in the prediction and design of the microstructure and properties of Ti alloys are examined. Finally, the main challenges are discussed, and future directions for ML and high-throughput technologies in materials science are proposed. It is hoped that this review offers useful insights into the integration of these emerging technologies, providing some guidance for future research and applications of Ti alloys.

Graphical abstract