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For the chosen thermodynamic model, the topology of the phase diagram depends on the thermodynamic parameters of the elements and the selected interaction energy parameters. A systematic variation of the energy parameters can reveal the nature of the phase diagrams obtained under the given conditions. In the present study, the quasi-chemical solution model, which accounts for short range order parameter up to the pair cluster, is adopted for the solid phase, while the regular solution model is used for the liquid phase. Using these solution models, the boundaries of the interaction energy parameters that result in various invariant reactions are calculated for the model system. The regions bounded by these boundaries are referred to as domains. This methodology is applied to the Cu-Ir system. The invariant reaction data obtained from these domains are used to train an artificial neural network model to predict the interaction energy parameters. The phase diagram obtained from the predicted values shows a good agreement with the experimental data. Thus, this methodology provides intelligent initial interaction parameters for the optimization process using experimental data.
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Gorrey et al. (2026) studied this question.
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