A Cluster-Based Computational Thermodynamics Framework with Intrinsic Chemical Short-Range Order: Part I. Configurational Contribution

Abstract

Exploiting chemical short-range order (SRO) is a promising new avenue for manipulating the properties of alloys. However, existing modeling frameworks are not sufficient to understand and predict SRO in multicomponent (>3) alloys. In this work, we developed a hybrid computational thermodynamics framework by marrying unique advantages from CVM (Cluster Variation Method) and CALPHAD (CALculation of PHAse Diagram) method through incorporating chemical SRO into CALPHAD with a novel cluster-based solution model. The key is to use the Fowler-Yang-Li transform to decompose the cumbersome cluster chemical potentials in CVM into fewer site chemical potentials of the basis cluster, thereby considerably reducing the number of variables that must be minimized for multicomponent systems. The new framework puts more physics, primarily intrinsic SRO, into CALPHAD, while maintaining its practicality and efficiency. It leverages statistical mechanics to yield a more physical description of configurational entropy and opens the door to cluster-based CALPHAD database development. The application of this newly proposed model in the prototype FCC AB system demonstrated that this model can correctly capture the essential features of the phase diagram and thermodynamic properties. The hybrid CVM-CALPHAD framework represents a new methodology for thermodynamic modeling that enables atomic-scale order to be exploited as a dimension for materials design, which potentially leads to novel complex concentrated alloys. It achieves a balance between the accuracy and computational cost for modeling multicomponent alloys with the intrinsic SRO in the context of CALPHAD