Machine Learning-Driven Prediction of Glass-Forming Ability in Fe-Based Bulk Metallic Glasses Using Thermophysical Features and Data Augmentation

Machine Learning-Driven Prediction of Glass-Forming Ability in Fe-Based Bulk Metallic Glasses Using Thermophysical Features and Data Augmentation

The identification of suitable alloy compositions for the formation of bulk metallic glasses (BMGs) is a key challenge in materials science. In this study, we developed machine learning (ML) models to predict the critical casting diameter (<inline-formula><math xmlns="http://www.w3.org...

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Bibliographic Details
Main Authors: Renato Dario Bashualdo Bobadilla, Marcello Baricco, Mauro Palumbo
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Metals
Subjects:
machine learning
BMGs
glass-forming ability (GFA)
CALPHAD approach
Online Access:https://www.mdpi.com/2075-4701/15/7/763
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