Composition-based phase stability model for multicomponent metal alloys

TL;DR

This paper introduces a rapid, thermodynamics-based computational model to evaluate the stability of multicomponent metal alloys, aiding materials discovery by efficiently narrowing down promising compositions.

Contribution

The authors develop a broadly applicable, accurate, and computationally inexpensive stability prediction model for multicomponent alloys using DFT data, improving upon previous heuristic methods.

Findings

Achieves 70-75% accuracy compared to experimental data.

Incorporates binary enthalpy and ideal configurational entropy in stability assessment.

Applicable to alloys with arbitrary elements, stoichiometry, and temperature.

Abstract

The vastness of the space of possible multicomponent metal alloys is hoped to provide improved structural materials but also challenges traditional, low-throughput materials design efforts. Computational screening could narrow this search space if models for materials stability and desired properties exist that are sufficiently inexpensive and accurate to efficiently guide experiments. Towards this effort, here we develop a method to rapidly assess the thermodynamic stability of a metal alloy composition of arbitrary number of elements, stoichiometry, and temperature based on density functional theory (DFT) data. In our model, the Gibbs free energy of the solid solution contains binary enthalpy contributions and ideal configurational entropy, whereas only enthalpy is considered for intermetallic competing phases. Compared to a past model for predicting the formation of single-phase high-entropy alloys [Phys. Rev. X 5, 011041 (2015)], our method is similarly inexpensive, since it assesses enthalpies based on existing DFT data, but less heuristic, more broadly applicable, and more accurate (70--75%) compared to experiment.