Materials genome strategy for metallic glasses

• Published in: Journal of materials science & technology Vol. 166; pp. 173 - 199
• Main Authors: Lu, Zhichao, Zhang, Yibo, Li, Wenyue, Wang, Jinyue, Liu, Xiongjun, Wu, Yuan, Wang, Hui, Ma, Dong, Lu, Zhaoping
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 10.12.2023
• Subjects: High-throughput techniques; Machine learning; Materials genome initiative; Metallic glasses; Metallic glasses; Materials genome initiative; Machine learning; High-throughput techniques
• ISSN: 1005-0302; 1941-1162
• DOI: 10.1016/j.jmst.2023.04.074

•A timely overview of key advances in materials genome strategy for metallic glasses (MGs) was presented.•Current challenges of high-throughput techniques and data-driven machine learning strategies for MGs were discussed in depth.•Future opportunities and perspectives for materials genome strategy-assisted design of MGs were proposed and surmised. Metallic glasses (MGs) have attracted extensive attention in the past decades due to their unique chemical, physical and mechanical properties promising for a wide range of engineering applications. A thorough understanding of their structure-property relationships is the key to the development of novel MGs with desirable performance. New strategies, as proposed by Materials Genome Initiative (MGI), construct a new paradigm for high-throughput materials discovery and design, and are being increasingly implemented in the search of new MGs. While a few reports have summarized the application of high-throughput and/or machine learning techniques, a comprehensive assessment of materials genome strategies for developing MGs is still missing. Herein, this paper aims to present a timely overview of key advances in this fascinating subject, as well as current challenges and future opportunities. A holistic approach is used to cover the related topics, including high-throughput preparation and characterization of MGs, and data-driven machine learning strategies for accelerating the development of novel MGs. Finally, future research directions and perspectives for MGI-assisted design of MGs are also proposed and surmised.