Mechanical properties of zirconium-based random alloys: Alloying elements and composition dependencies

Huang, Sunchao; Zhang, Yongsheng; Zhang, Xiaoli; Wang, Zongguo; Yang, Xiaoyu; Zhang, Zhi

doi:10.1016/j.commatsci.2016.10.028

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…The traditional compositional design strategy of alloys always begins with one (or rarely two) principal elements and proceeds by addition of various alloying elements to tailor desired properties. [1][2][3] Typically, the intrinsic properties of the designed alloy remain dominated by the principal element. For example, Fe acts as the principal element in steels, Ni/Co in superalloys and Ti in titanium alloys.…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted design of high entropy alloys with desired property

et al. 2019

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Section: Introductionmentioning

confidence: 99%

Machine learning assisted design of high entropy alloys with desired property

et al. 2019

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“…The conventional alloying method almost always starts with one or two principal metallic elements and advances by incorporation of different alloying elements to engineer desired mechanical and chemical properties [1][2][3] . Therefore, the mechanical and chemical properties of the synthesized alloy remain controlled by the principal elements.…”

mentioning

confidence: 99%

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

Khakurel

Taufique

Roy

et al. 2021

Sci Rep

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We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

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“…Nanostructures have attracted great attention from both the academic and industrial areas over the past few decades due to their properties, which are different from those conventional materials. As of today, they have been used in many fields such as catalysis, − environmental protection, , and biomedicine. , It has been widely established that the crystal structure, composition, shape, and atomic distribution of nanoalloys dictate their properties and functions. − Notably, an effective way to realize a specific functionality is to synthesize alloyed nanostructures with well-defined shapes . Thanks to the continuous development of synthetic techniques, the morphologies of nanostructures synthesized experimentally are diverse, ranging from nanospheres to nanopolyhedrons, nanorods, nanorings, and nanoframes …”

Section: Introductionmentioning

confidence: 99%

Thermally Activated Microstructural Evolution of PtIrCu Alloyed Nanorings: Insights from Molecular Dynamics Simulations

Yang

Pan

et al. 2022

ACS Omega

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Nanoalloys have attracted extensive interest from the research and industrial community due to their unique properties. In this work, the thermally activated microstructural evolution and resultant collapse of PtIrCu nanorings were investigated using molecular dynamics simulations. Three PtIrCu nanorings with a fixed outer radius and varied inner radii were addressed to investigate the size effects on their thermal and shape stabilities. The shape factor was introduced to monitor their shape changes, and a common neighbor analysis was employed to characterize the local structures of atoms. The results reveal that both the thermal and shape stabilities of these nanorings can be remarkably improved by decreasing the inner radius. Furthermore, they all experienced the evolutionary process from ring to pie and spherelike morphologies, finally resulting in structural collapse. The stacking faults were observed in these rings during the heating process. Our work sheds light on the fundamental understanding of alloyed nanorings subjected to heating, hence offering a theoretical foundation for their syntheses and applications.

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Mechanical properties of zirconium-based random alloys: Alloying elements and composition dependencies

Cited by 7 publications

References 25 publications

Machine learning assisted design of high entropy alloys with desired property

Machine learning assisted design of high entropy alloys with desired property

Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys

Thermally Activated Microstructural Evolution of PtIrCu Alloyed Nanorings: Insights from Molecular Dynamics Simulations

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