Simulating high-entropy alloys at finite temperatures: An uncertainty-based approach

A general method is presented for modeling high-entropy alloys as ensembles of randomly sampled, ordered configurations on a given lattice. Statistical mechanics is applied post hoc to derive the ensemble properties as a function of composition and temperature, including the free energy of mixing and local structure. Random sampling is employed to address the high computational costs needed to model alloys with a large number of components. Doing so also provides rigorous convergence criteria, including the quantification of noise due to random sampling, and an estimation of the number of additional samples required to lower this noise to the desired levels. Binary to five-component alloys of the group-IV chalcogenides are used as case examples, for which the predicted miscibility shows excellent agreement with experiment. This method is well-suited for calculating the configurational thermodynamics, local structure, and ensemble properties of complex alloys, and it is attractive for materials with temperature-dependent, short-range order.