The Mixing Thermodynamics and Local Structure of High-entropy Alloys from Randomly Sampled Ordered Configurations

TL;DR

This paper introduces a computational method for modeling high entropy alloys using random sampling of configurations, enabling efficient prediction of thermodynamic and structural properties across various alloy types.

Contribution

The authors develop a general, statistically rigorous approach that combines random sampling with statistical mechanics to model complex alloys efficiently and accurately.

Findings

Predicted alloy miscibility aligns well with experimental data.

Method effectively handles alloys with many components and residual short-range order.

Provides convergence criteria and noise quantification for sampling accuracy.

Abstract

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 needed/desired levels. This method is well-suited for a variety of cases: i) high entropy alloys, where standard lattice models are costly; ii) "medium" entropy alloys, where both the entropy and enthalpy play significant roles; and iii) alloys with residual short-range order. Binary to 5-component alloys of the group-IV chalcogenides are used as case examples, for which the predicted miscibility shows excellent agreement with experiment.