Effect of Ho Addition on the Glass-Forming Ability and Crystallization Behaviors of Zr54Cu29Al10Ni7 Bulk Metallic Glass

The interaction of metallic glasses (MGs) with hydrogen can trigger many interesting physical, chemical and mechanical phenomena. However, atomic-scale understanding is still lacking. In this work, molecular dynamics (MD) simulations are employed to study the atomic structure, mechanical properties and relaxation behaviors of H-doped Ni50Al50 MGs doped by two methods. The properties of H-doped MGs are determined not only by the hydrogen content but also by the doping method. When H atoms are doped into the molten state of samples, H atoms can fully diffuse and interact with metallic atoms, resulting in loose local atomic structures, homogeneous deformation and enhanced β relaxation. In contrast, when H atoms are doped into as-cast MGs, the H content is crucial in affecting the atomic structure and mechanical properties. A small number of H atoms has little influence on the elastic matrix, while the percolation of shear transformation zones (STZs) is hindered by H atoms, resulting in the delay of shear band (SB) formation and an insignificant change in the strength. However, a large number of H atoms can make the elastic matrix loose, leading to the decrease in strength and the transition of the deformation mode from SB to homogeneous deformation. The H effects on the elastic matrix and flow units are also applied to the dynamic relaxation. The deformability of H-doped Ni50Al50 MGs is enhanced by both H-doping methods; however, our results reveal that the mechanisms are different.

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Influence of the Hydrogen Doping Method on the Atomic Structure, Mechanical Properties and Relaxation Behaviors of Metallic Glasses

Zhang

Gao

Zhang

2023

Materials

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Effect of Yttrium Doping on Glass-Forming Ability, Thermal Stability, and Corrosion Resistance of Zr50.7Cu28Ni9Al12.3 Bulk Metallic Glass

Zhou

Zhao

et al. 2023

Metals

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Metallic glasses (MGs) possess excellent corrosion resistance due to their amorphous nature. Zr-Cu-Ni-Al is one of the most investigated metallic glass (MG) systems due to its excellent corrosion resistance, relatively low cost, and nontoxic qualities. Yet the application of the Zr-Cu-Ni-Al MGs is still limited by its glass forming ability (GFA). It has been found that the doping of yttrium can purify the Zr-Cu-Ni-Al melt and increase GFA. However, research on the yttrium doping’s corrosion resistance is still lacking. In the present work, (Zr50.7Cu28Ni9Al12.3)100−xYx (x = 0 (Y0), 1 (Y1), 2 (Y2), 3 (Y3)) alloys were prepared, and the effects of the yttrium doping on the thermodynamic parameters, crystallization activation energy, and corrosion resistance were studied. It was found that the doping of yttrium can improve the GFA and the crystallization-activation energy of the Zr50.7Cu28Ni9Al12.3 MG. The largest GFA is obtained when the yttrium-doping content reaches 2 at.%. The parameter γ is found to be most suitable for the GFA evaluation of the (Zr50.7Cu28Ni9Al12.3)100−xYx alloy system. Y2 alloy exhibits the best corrosion resistance among the Y0–Y3 alloys according to the potentiodynamic polarization results. The simultaneous increase of the GFA, thermal stability, and corrosion resistance was achieved in the Zr50.7Cu28Ni9Al12.3 MG by proper yttrium doping.

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Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass

Liu,

Wang,

Cai

et al. 2023

Processes

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The prediction of the glass-forming ability (GFA) of metallic glasses (MGs) can accelerate the efficiency of their development. In this paper, a dataset was constructed using experimental data collected from the literature and books, and a machine learning-based predictive model was established to predict the GFA. Firstly, a classification model based on the size of the critical diameter (Dmax) was established to determine whether an alloy system could form a glass state, with an accuracy rating of 0.98. Then, regression models were established to predict the crystallization temperature (Tx), glass transition temperature (Tg), and liquidus temperature (Tl) of MGs. The R2 of the prediction model obtained in the test set was greater than 0.89, which showed that the model had good prediction accuracy. The key features used by the regression models were analyzed using variance, correlation, embedding, recursive, and exhaustive methods to select the most important features. Furthermore, to improve the interpretability of the prediction model, feature importance, partial dependence plot (PDP), and individual conditional expectation (ICE) methods were used for visualization analysis, demonstrating how features affect the target variables. Finally, taking Zr-Cu-Ni-Al system MGs as an example, a prediction model was established using a genetic algorithm to optimize the alloy composition for high GFA in the compositional space, achieving the optimal design of alloy composition.

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Effect of Ho Addition on the Glass-Forming Ability and Crystallization Behaviors of Zr54Cu29Al10Ni7 Bulk Metallic Glass

Cited by 3 publications

References 46 publications

Influence of the Hydrogen Doping Method on the Atomic Structure, Mechanical Properties and Relaxation Behaviors of Metallic Glasses

Influence of the Hydrogen Doping Method on the Atomic Structure, Mechanical Properties and Relaxation Behaviors of Metallic Glasses

Effect of Yttrium Doping on Glass-Forming Ability, Thermal Stability, and Corrosion Resistance of Zr50.7Cu28Ni9Al12.3 Bulk Metallic Glass

Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass

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