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    Machine Learning-Driven Prediction of Glass-Forming Ability in Fe-Based Bulk Metallic Glasses Using Thermophysical Features and Data Augmentation by Renato Dario Bashualdo Bobadilla, Marcello Baricco, Mauro Palumbo

    Published 2025-07-01
    “…In this study, we developed machine learning (ML) models to predict the critical casting diameter (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>D</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></semantics></math></inline-formula>) of Fe-based BMGs, enabling rapid assessment of glass-forming ability (GFA) using composition-based and calculated thermophysical features. …”
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