How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?
This paper demonstrates that a hybrid machine learning approach, which embeds physically informed elemental descriptors into the Redlich-Kister framework, effectively overcomes the data limitations of traditional CALPHAD modeling to enable robust, zero-shot prediction of thermodynamic interaction parameters for unknown or data-scarce alloy systems.