CrCoNiAlxTiy高熵合金层错能及力学性能的微观模拟

IF 9.5 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Yuzhu Zhao, Jinkai Qiu, Siying Liu, Mengde Kang, Tiwen Lu, Cheng Lian, Xiancheng Zhang and Honglai Liu
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引用次数: 0

摘要

基于crconi的高熵合金(HEAs)因其优异的强度、耐磨性、塑性和韧性而受到越来越多的关注。然而,如何同时优化强度和塑性,特别是在低温条件下,仍然是一个具有挑战性的材料设计问题。微观模拟是通过模拟HEAs复杂微观结构来了解其层错特性和力学特性的重要手段。本文以CrCoNiAlxTiy HEAs为研究对象,采用分子动力学(MD)模拟和密度泛函理论(DFT)计算分析了其塑性-韧性平衡。研究了Al和Ti掺杂对合金微观结构、层错能和杨氏模量的影响。结果表明,高Ti含量降低了材料的SFE和杨氏模量,同时提高了材料的塑性韧性,而高Al含量往往会增加SFE并降低杨氏模量,共同影响CrCoNiAlxTiy HEAs的力学性能。不同温度下的应力应变分析表明,低温下的力学性能有所改善。此外,机器学习(ML)结果表明,XGBT模型最能估计HEAs的力学性能,得到的R2最接近1,RMSE最小。这项工作为HEAs中成分依赖的变形行为提供了机制理解,并为极端环境(如航空航天和极地应用)中的合金设计提供了理论指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Microscopic simulation of stacking fault energy and mechanical properties in CrCoNiAlxTiy high-entropy alloys†

Microscopic simulation of stacking fault energy and mechanical properties in CrCoNiAlxTiy high-entropy alloys†

Microscopic simulation of stacking fault energy and mechanical properties in CrCoNiAlxTiy high-entropy alloys†

CrCoNi-based high-entropy alloys(HEAs) have garnered increasing attention due to their superior strength, wear resistance, plasticity, and toughness. However, how to simultaneously optimize strength and plasticity, especially under cryogenic conditions, remains a challenging materials design problem. Microscopic simulations are essential for understanding stacking fault properties and mechanical properties of HEAs by modeling their complex microstructures. This study focuses on CrCoNiAlxTiy HEAs, analyzing their plasticity–toughness balance using molecular dynamics (MD) simulation and density functional theory (DFT) calculation. The effects of Al and Ti doping on microstructure, stacking fault energy (SFE) and Young's modulus were investigated, respectively. Results indicate high Ti content decreases SFE and Young's modulus while enhancing the material's plastic toughness, whereas high Al content tends to increase the SFE and also reduce the Young's modulus, collectively influencing the mechanical properties of CrCoNiAlxTiy HEAs. Stress–strain analysis at different temperatures reveals improved mechanical properties at low temperatures. In addition, Machine Learning (ML) results show that the XGBT model best estimates the mechanical properties of HEAs, with the resulting R2 closest to 1 and the smallest RMSE. This work offers a mechanistic understanding of composition-dependent deformation behavior in HEAs and provides theoretical guidance for alloy design in extreme environments such as aerospace and polar applications.

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来源期刊
Journal of Materials Chemistry A
Journal of Materials Chemistry A CHEMISTRY, PHYSICAL-ENERGY & FUELS
CiteScore
19.50
自引率
5.00%
发文量
1892
审稿时长
1.5 months
期刊介绍: The Journal of Materials Chemistry A, B & C covers a wide range of high-quality studies in the field of materials chemistry, with each section focusing on specific applications of the materials studied. Journal of Materials Chemistry A emphasizes applications in energy and sustainability, including topics such as artificial photosynthesis, batteries, and fuel cells. Journal of Materials Chemistry B focuses on applications in biology and medicine, while Journal of Materials Chemistry C covers applications in optical, magnetic, and electronic devices. Example topic areas within the scope of Journal of Materials Chemistry A include catalysis, green/sustainable materials, sensors, and water treatment, among others.
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