| 1. |
- Huang, He, et al.
(författare)
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Critical stress for twinning nucleation in CrCoNi-based medium and high entropy alloys
- 2018
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Ingår i: Acta Materialia. - : PERGAMON-ELSEVIER SCIENCE LTD. - 1359-6454 .- 1873-2453. ; 149, s. 388-396
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Tidskriftsartikel (refereegranskat)abstract
- The CrCoNi-based medium and high entropy alloys (MHEAs) have drawn much attention due to their exceptional mechanical properties at cryogenic temperatures. The twinning critical resolved shear stress (CRSS) is a fundamental parameter for evaluating the strength-ductility properties of MHEAs. Here we construct and apply an extended twinning nucleation Peierls-Nabarro (P-N) model to predict the twinning CRSSes of face-centered cubic (FCC) CrCoNi-based MHEAs. The order of the twinning CRSSes of the selected alloys is CrCoNi > CrCoNiMn > CrCoNiFe > CrCoNiFeMn and the values are 291, 277, 274 and 236 MPa, respectively. These theoretical predictions agree very well with the experimental twinning CRSSes of CrCoNi and CrCoNiFeMn accounting for 260 +/- 30 and 235 +/- 10 MPa, respectively and are perfectly consistent with the strength-ductility properties including yield stress, ultimate tensile stress and uniform elongation for fracture of the FCC CrCoNi-based MHEAs obtained at cryogenic temperatures. The present method offers a first-principle quantum-mechanical tool for optimizing and designing new MHEAs with exceptional mechanical properties.
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| 2. |
- Huang, He, et al.
(författare)
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Material informatics for uranium-bearing equiatomic disordered solid solution alloys
- 2021
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Ingår i: Materials Today Communications. - : Elsevier BV. - 2352-4928. ; 29
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Tidskriftsartikel (refereegranskat)abstract
- Near-equiatomic, multi-component alloys with disordered solid solution phase (DSSP) are associated with outstanding performance in phase stability, mechanical properties and irradiation resistance, and may provide a feasible solution for developing novel uranium-based alloys with better fuel capacity. In this work, we build a machine learning (ML) model of disordered solid solution alloys (DSSAs) based on about 6000 known multicomponent alloys and several materials descriptors to efficiently predict the DSSAs formation ability. To fully optimize the ML model, we develop a multi-algorithm cross-verification approach in combination with the SHapley Additive exPlanations value (SHAP value). We find that the Delta S-C, Lambda, Phi(s), gamma and 1/Omega, corresponding to the former two Hume - Rothery (H - R) rules, are the most important materials descriptors affecting DSSAs formation ability. When the ML model is applied to the 375 uranium-bearing DSSAs, 190 of them are predicted to be the DSSAs never known before. 20 of these alloys were randomly synthesized and characterized. Our predictions are in-line with experiments with 3 inconsistent cases, suggesting that our strategy offers a fast and accurate way to predict novel multi-component alloys with high DSSAs formation ability. These findings shed considerable light on the mapping between the material descriptors and DSSAs formation ability.
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