SPEA -- an analytical thermodynamic model for defect phase diagram

TL;DR

The paper introduces SPEA, an analytical thermodynamic model that accurately predicts defect phase transformations and surface phase diagrams in metal alloys, validated against Monte Carlo simulations.

Contribution

SPEA is a new analytical model that efficiently predicts defect phase transformations, outperforming traditional sublattice models in accuracy and computational cost.

Findings

SPEA accurately reproduces Monte Carlo simulation results.

It correctly predicts surface order-disorder transitions.

SPEA outperforms the sublattice model in describing phase behavior.

Abstract

We propose an analytical thermodynamic model for describing defect phase transformations, which we term the statistical phase evaluation approach (SPEA). The SPEA model assumes a Boltzmann distribution of finite size phase fractions and calculates their statistical average. To benchmark the performance of the model, we apply it to construct binary surface phase diagrams of metal alloys. Two alloy systems are considered: a Mg surface with Ca substitutions and a Ni surface with Nb substitutions. To construct a firm basis against which the performance of the analytical model can be leveled, we first perform Monte Carlo (MC) simulations coupled with cluster expansion of density functional theory dataset. We then demonstrate the SPEA model to reproduce the MC results accurately. Specifically, it correctly predicts the surface order-disorder transitions as well as the coexistence of the 1/3 ordered phase and the disordered phase. Finally, we compare the SPEA method to the sublattice model commonly used in the CALPHAD approach to describe ordered and random solution phases and their transitions. The proposed SPEA model provides a highly efficient approach for modeling defect phase transformations.