Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. The stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young’s, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior.
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Performance-oriented multistage design for multi-principal element alloys with low cost yet high efficiency
Multi-principal element alloys (MPEAs) with remarkable performances possess great potential as structural, functional, and smart materials. However, their efficient performance-orientated design in a wide range of compositions and types is an extremely challenging issue, because of properties strongly dependent upon the composition and composition-dominated microstructure. Here, we propose a multistage-design approach integrating machine learning, physical laws and a mathematical model for developing the desired-property MPEAs in a very time-efficient way. Compared to the existing physical model- or machine-learning-assisted material development, the forward-and-inverse problems, including identifying the target property and unearthing the optimal composition, can be tackled with better efficiency and higher accuracy using our proposed avenue, which defeats the one-step component-performance design strategy by multistage-design coupling constraints. Furthermore, we developed a new multi-phase MPEA at the minimal time and cost, whose high strength-ductility synergy exceeded those of its system and subsystem reported so far by searching for the optimal combination of phase fraction and composition. The present work suggests that the property-guided composition and microstructure are precisely tailored through the newly built approach with significant reductions of the development period and cost, which is readily extendable to other multi-principal element materials.
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- Award ID(s):
- 1809640
- NSF-PAR ID:
- 10343679
- Date Published:
- Journal Name:
- Materials Horizons
- Volume:
- 9
- Issue:
- 5
- ISSN:
- 2051-6347
- Page Range / eLocation ID:
- 1518 to 1525
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D1MH01912K
