Abstract: Metallic glasses (MGs) have attracted extensive attention in the past decades due to their unique chem-ical, physical and mechanical properties promising for a wide range of engineering applications. A thor-ough 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 increas-ingly 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. Fi-nally, future research directions and perspectives for MGI-assisted design of MGs are also proposed and surmised.

Key words: Metallic glasses, Materials genome initiative, High-throughput techniques, Machine learning