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This work presents an interpretable machine learning (ML) system that uses composition- and physics-based descriptors to predict the bulk moduli of high-entropy alloys (HEAs) by permitting precise and computationally efficient bulk modulus prediction, as well as physically significant insights into descriptor–property connections.
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