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This study presents a novel approach for analyzing and establishing LargeCALPHAD model (LCM) in complex alloy systems. Through the introduction of"composition space volume", a multi-dimensional metric which allows toquatitatively define alloy composition variations. Utilizing stochasticmethods, the study quantifies phase space complexity through phase density, andmodel training costs through data density. This leads to a strategicsegmentation of the entire composition space, tailored to the complexity ofeach segment, thereby reducing computational efforts in model training. Asignificant advancement is the integration of segmented models using a Mixtureof Experts (MoE) approach, ensuring accurate portrayal of phase behaviorsacross diverse composition spaces. This technique is demonstrated inestablishing a high-dimensional phase diagram for the FeCoNiTi system,highlighting its efficiency and accuracy. The study's methodologies offer asystematic and cost-effective framework for modeling complex alloy systems,marking a step forward in the field of alloy design and analysis.

Uncovering high-dimensional phase space and the application of Mixture of Experts (MoE) on building the Large CALPHAD Model (LCM)