The work presented in this paper addresses a data gap that continues to be a hinderance to users of precipitation modeling tools, particularly those based on Langer-Schwartz theory. Thermodynamic and kinetic data required for precipitation models can be obtained from CALPHAD databases, but interfacial energies between the bulk and precipitate phases are not available for many alloy systems. In this work, a number of matrix-precipitate interfacial energies have been determined for influential precipitates in alloys of industrial importance, for example, carbides in Grade 22 low-alloy steels, delta phase in Ni 625 and 718, S-phase in Al 2024, and Q’ and β’’ in Al 6111.

Traditionally, chemists and metallurgists have used phase diagrams and tables of thermodynamic data for understanding and making predictions related to alloy development and process improvement, such as heat treatment. However, for complex, multi-component systems that extend beyond ternaries, such an approach can be limiting. Computational thermodynamics and specifically the CALPHAD approach allows for the prediction of the thermodynamic properties and phase equilibria of multi-component, multi-phase systems based on mathematical models that describe the Gibbs energy as a function of temperature, pressure and composition for each individual phase in a system. Parameters in the numerical models capture the composition and temperature dependence in binary and ternary systems and are optimized in order to best correspond to the experimental data available and are stored in databases which are then used in conjunction with computer codes whereby extrapolations can be made into the multi-component systems of interest. Additionally, the CALPHAD method can also be extended to model atomic mobilities and diffusivities in a similar way. Thermodynamic and kinetic databases are developed through a hybrid of experiments, first-principles calculations and CALPHAD modelling. By combining the thermodynamic and mobility databases, kinetic reactions during solidification and subsequent heat treatment processes can then be simulated. Through the use of such simulations it is possible to optimize alloy compositions and predict optimal solidification processes and solution heat treatment temperature ranges without performing many time-consuming and costly experiments.