Adisa Rasak

Data-driven prediction and classification of multi-component alloys using interpretable machine learning

This study presents an integrated, interpretable machine learning framework applied to a dataset of 2,672 multi-component alloy systems, using elemental composition as the sole input, providing a scalable and interpretable foundation for accelerated alloy screening, property prediction, and data-driven materials discov...