Towards intelligent design of metallic glasses: a data-driven pathway for closing the theory-experiment loop

As advanced amorphous materials with superior mechanical, physical, and chemical properties, metallic glasses (MGs) hold significant promise for a wide range of applications. However, their rational design and precise property control have long been impeded by notable challenges. These obstacles largely arise from the inherent compositional and structural complexity of MGs, which not only slows empirical trial-and-error experimentation but also limits the scalability of computationally intensive first-principles simulations. In recent years, machine learning has emerged as a transformative tool, offering unprecedented capabilities to decode these intricate relationships and overcome conventional research limitations. Here we provide a systematic overview of machine-learning-guided investigations of MGs and their associated data pipelines, centering on two key paradigms: the feature-driven ‘Keplerian’ data pipeline and a next-generation theory-experiment-aligned pipeline designed to close the gap between simulation and experiment. By breaking down both paradigms into modular workflow components, we underscore the essential roles of data standardization, interpretable feature engineering, and physics-informed validation in constructing a reliable research framework. We anticipate that a sufficiently robust, efficient, and generalizable data pipeline will not only unlock novel scientific insights and accelerate material discovery but also propel the field from a ‘trial-and-error’ approach toward an era of intelligent and principled design.