FeCrAl alloys serve as promising nuclear fuel cladding materials due to their exceptional oxidation resistance and irradiation damage tolerance. These superior properties stem from their optimal chemical composition, particularly the concentrations of Cr and Al as critical alloying elements. Consequently, exploring the compositional space of FeCrAl alloys using efficient and accurate methods is crucial for designing high-performance variants. In this work, we developed a machine learning interatomic potential for FeCrAl alloys based on the Deep Potential (DP) framework. Utilizing this potential, we performed DP molecular dynamics simulations to systematically investigate the elastic properties and uniaxial tensile behavior in FeCrAl alloys with varying compositions. These studies elucidate that the Cr content exhibits a relatively minor influence on the mechanical properties of FeCrAl alloys, while the Al content shows a significant impact instead. Based on our study, a composition of 13–14 wt. % Cr and approximately 3–4 wt. % Al is beneficial for enhancing the mechanical properties of the FeCrAl alloys.
Titanium alloys have found extensive application in the aerospace field due to their outstanding properties. However, traditional material design methods involve extensive computational and experimental trial-and-error processes, leading to increased design costs and reduced efficiency. This paper proposes a machine le...
Shuang Yu, Yu-Wei Du, Tong Wang et al.· Journal of Physics, Conferen...· 0 citations
Lightweight alloys with superior strength-ductility synergies are crucial for advanced structural applications, and nanostructured titanium (Ti) alloys are promising candidates. However, it remains challenging to manufacture bulk components with complex geometries as well as preserving the nanostructure and associated...
In this work, a neuro-evolution machine learning potential integrated with the Ziegler-Biersack-Littmark repulsive potential is developed for accurately describing defect-dependent mechanical behaviors of molybdenum-niobium (Mo-Nb) alloys using only 721 training structures. Molecular dynamics simulations are then perfo...
Ye-Xin Yang, Lu Sun, Ao-Xiang Gong et al.· Journal of Chemical Physics· 0 citations
A comprehensive database was constructed, comprising over twenty thousand hardness values from steels with various compositions and nitriding parameters, and two predictive models were developed incorporating physically based parameters such as nitrogen diffusion distance and nitriding layer thickness.
Sayaka Sekida, G. Miyamoto, Kenji Nagata et al.· Metallurgical and Materials...· 0 citations
Preparation of fine-grain, low-microsegregation Ti-rich refractory high-entropy alloys (RHEAs) remains challenging for conventional techniques. Additive manufacturing (AM)/3D printing can weaken the microsegregation but its strong directional heat flux is unfavorable to formation of fine equiaxed grains. Refining grain...
Shi-En Liu, Yan Wang, Yao-Jian Liang et al.· Microstructures· 0 citations
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