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In the present study, we investigate the performance of efficient pair potentials in comparison to accurate ab initio potentials as energy descriptions for Monte Carlo simulations of solid-state precipitation. As test scenario, we take the phase decomposition kinetics in binary Fe1-xCux. In a first effort, we predict thermodynamic equilibrium properties of bcc-rich Cu precipitates in an Fe-rich solution with a temperature and composition dependent Cluster Expansion. For this Cluster Expansion, combined ab inito and phonon calculations for various configurations serve as input. Alternatively, we apply the Local Chemical Environment approach, where the energy is described by computationally efficient pair potentials, which are calibrated on the first principles cluster expansion results. We observe that these fundamentally different approaches provide similar information in terms of the precipitate radius, chemical composition and interface constitution, however, the computational effort for the Local Chemical environment approach is significantly lower.

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Phase decomposition in binary Fe1−x Cu x is studied using Monte Carlo simulations. Initially, density functional theory calculations are utilized to determine reference energies of various Fe–Cu compounds that serve as input for a temperature and composition-dependent cluster expansion. On this basis, the thermodynamic properties of the bcc Fe–Cu system are predicted and used to simulate the equilibrium constitution of bcc Cu-rich precipitates in an Fe-rich solid solution at various temperatures and supersaturations. Complementarily, computationally efficient pair potentials are developed in the local chemical environment approach that are calibrated on the first principles-cluster expansion results. These are then utilized in large-scale simulations for analysis of the multi-particle precipitate evolution. It is concluded that both approaches provide comparable information in terms of the precipitate radius as well as interface constitution. Whereas the cluster expansion (‘full-information’) path is especially useful in predicting energies of various ground state configurations for small systems, the local chemical environment approach (‘fast-computation’) path is particularly useful in evaluation of cluster formation kinetics and evolution statistics.

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