基于深势分子动力学的CrCoFeNiMn高熵合金力学性能优化非晶化演化研究

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Wentao Zhou, Jia Song, Lve Lin, Huilong Yang, Shaoqiang Guo, Guang Ran, Yafei Wang
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引用次数: 0

摘要

本研究采用机器学习驱动的深势分子动力学(DPMD)模拟方法,研究了不同过冷速率下康托高熵合金(HEA)的组织演变及其与力学性能变化的相关性。研究结果揭示了康托合金非晶-结晶转变的临界过冷速率,以及在过冷过程中不同温度下的局部组织构成。相关的力学性能研究表明,在高过冷速率下非晶化的康托合金具有优异的塑性性能,但这种性能与非晶化冷却速率无关。虽然康托合金的高强度需要较低的过冷速率,而过冷速率可能导致非晶化,但在非晶化-结晶转变的临界过冷速率下对康托合金进行非晶化可以同时兼顾延性和强度。这一发现为材料的发展及其工业应用的机械性能优化提供了新的思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Amorphization evolution study of CrCoFeNiMn high entropy alloy for mechanical performance optimization by deep potential molecular dynamics

Amorphization evolution study of CrCoFeNiMn high entropy alloy for mechanical performance optimization by deep potential molecular dynamics

In the study, we explore the structural evolution of Cantor high-entropy alloy (HEA) under different super-cooling rates and its correlation with mechanical property variations by the developed machine learning-driven deep potential molecular dynamics (DPMD) simulation. Our results reveal the critical super-cooling rate of amorphization-crystallization transition of Cantor alloy and the local structure constitutions at different temperatures during the super-cooling process. The associated mechanical property studies demonstrate the glassy Cantor alloy amorphized at high super-cooling rate exhibits a superior capability of ductility but this capability is unrelated to the amorphization cooling rates. While the high strength of Cantor alloy requires a lower super-cooling rate which might result in the crystallization, amorphizing the Cantor alloy at the critical super-cooling rate of amorphization-crystallization transition could compatibilize both ductility and strength capabilities. Such a discovery sheds new lights on the material development and its mechanical performance optimization for industrial applications.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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