The coupling of carbon non-stoichiometry and short-range order in governing mechanical properties of high-entropy ceramics

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Wenyu Lu, Jingru Xu, Shasha Huang, Xuepeng Xiang, Haijun Fu, Xinlei Gu, Baichuan Xu, Ailin Yang, Zhenggang Wu, Shijun Zhao
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Abstract

High-entropy carbide ceramics (HECCs) commonly exhibit non-stoichiometric compositions and short-range order (SRO) arising from diverse elemental mixing. In this study, taking (TiZrHfNb)C as a representative HECC, we explore the coupling effects of SRO and carbon non-stoichiometry based on density-functional theory (DFT) and machine learning (ML). DFT results indicate that carbon non-stoichiometry is favored in Ti and Nb environments due to enhanced local atomic relaxation and charge transfer, which contribute to improved d-d bonding interactions. DFT-based Monte Carlo (MC) simulations further reveal a clustering tendency of Ti and Nb elements that compete with carbon non-stoichiometry formation. These local features are effectively captured by ML models, enabling rapid assessment of the interplay among carbon deficiency, SRO, and their influences on the mechanical properties of HECCs. This work elucidates the microscopic local properties responsible for the macroscopic behavior, offering key insights for designing HECCs through careful element selection and local chemistry control.

Abstract Image

高熵陶瓷力学性能中碳非化学计量与短程有序的耦合作用
高熵碳化物陶瓷(HECCs)通常表现出非化学计量成分和由不同元素混合引起的短程有序(SRO)。本研究以(TiZrHfNb)C为代表的HECC,基于密度泛函理论(DFT)和机器学习(ML),探讨了SRO与碳非化学计量学的耦合效应。DFT结果表明,在Ti和Nb环境中,碳的非化学计量学更有利,因为局部原子弛豫和电荷转移增强,这有助于改善d-d键相互作用。基于dft的蒙特卡罗(MC)模拟进一步揭示了Ti和Nb元素与碳非化学计量形成竞争的聚类趋势。ML模型可以有效地捕获这些局部特征,从而能够快速评估碳缺乏、SRO之间的相互作用及其对hecc力学性能的影响。这项工作阐明了负责宏观行为的微观局部特性,为通过仔细的元素选择和局部化学控制来设计hecc提供了关键见解。
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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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