Tunable stacking fault energies by tailoring local chemical order in CrCoNi medium-entropy alloys

TL;DR

This study uses first-principles calculations to show how local chemical order in CrCoNi medium-entropy alloys can be tuned to control stacking fault energies, influencing their mechanical properties and phase stability.

Contribution

It reveals the relationship between local chemical order and stacking fault energies in HEAs, providing a pathway to tailor mechanical behavior through compositional tuning.

Findings

Intrinsic and extrinsic SFE are highly tunable by local chemical order.

Chemical order correlates with phase stability between fcc and hcp structures.

Chemical short-range order can be engineered to optimize alloy properties.

Abstract

High-entropy alloys (HEAs) are an intriguing new class of metallic materials due to their unique mechanical behavior. Achieving a detailed understanding of structure-property relationships in these materials has been challenged by the compositional disorder that underlies their unique mechanical behavior. Accordingly, in this work, we employ first-principles calculations to investigate the nature of local chemical order and establish its relationship to the intrinsic and extrinsic stacking fault energy (SFE) in CrCoNi medium-entropy solid-solution alloys, whose combination of strength, ductility and toughness properties approach the best on record. We find that the average intrinsic and extrinsic SFE are both highly tunable, with values ranging from -43 mJ.m-2 to 30 mJ.m-2 and from -28 mJ.m-2 to 66 mJ.m-2, respectively, as the degree of local chemical order increases. The state of local ordering also strongly correlates with the energy difference between the face-centered cubic (fcc) and hexagonal-close packed (hcp) phases, which affects the occurrence of transformation-induced plasticity. This theoretical study demonstrates that chemical short-range order is thermodynamically favored in HEAs and can be tuned to affect the mechanical behavior of these alloys. It thus addresses the pressing need to establish robust processing-structure-property relationships to guide the science-based design of new HEAs with targeted mechanical behavior.