Dynamic bond reconfiguration drives crack-tip carbon migration to strengthen iron grain boundaries

IF 13.1 1区材料科学 Q1 CHEMISTRY, PHYSICAL

npj Computational Materials Pub Date : 2026-10-02 DOI:10.1038/s41524-026-02352-y

Kazuma Ito, Takashi Otaki, Yuta Yoshimoto, Naoki Matsumura

{"title":"Dynamic bond reconfiguration drives crack-tip carbon migration to strengthen iron grain boundaries","authors":"Kazuma Ito, Takashi Otaki, Yuta Yoshimoto, Naoki Matsumura","doi":"10.1038/s41524-026-02352-y","DOIUrl":null,"url":null,"abstract":"Developing stronger alloys is essential for structural applications, but their reliability is limited by brittle cracking along general grain boundaries (GGBs). Solute segregation can control grain-boundary fracture, yet its effects are typically interpreted in terms of static cohesion changes before and after interfacial separation. How trace solutes modify moving-crack resistance at GGBs remains unclear because realistic segregation and crack propagation are difficult to treat atomistically. Here, we develop a fast and transferable Fe–C machine-learning interatomic potential. Using simulations of experimentally consistent carbon-segregated α-Fe GGBs, we show that trace carbon markedly increases crack-growth resistance, consistent with observed transition from intergranular fracture to cleavage. Carbon follows the moving crack tip through repeated breaking and reforming of Fe–C bonds, enriching the crack-tip region. We term this process dynamic bond reconfiguration (DBR). DBR dynamically amplifies static cohesive strengthening, revealing dynamic solute–crack-tip interactions as a framework for controlling brittle fracture in alloys.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"105 1","pages":""},"PeriodicalIF":13.1000,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Computational Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1038/s41524-026-02352-y","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Developing stronger alloys is essential for structural applications, but their reliability is limited by brittle cracking along general grain boundaries (GGBs). Solute segregation can control grain-boundary fracture, yet its effects are typically interpreted in terms of static cohesion changes before and after interfacial separation. How trace solutes modify moving-crack resistance at GGBs remains unclear because realistic segregation and crack propagation are difficult to treat atomistically. Here, we develop a fast and transferable Fe–C machine-learning interatomic potential. Using simulations of experimentally consistent carbon-segregated α-Fe GGBs, we show that trace carbon markedly increases crack-growth resistance, consistent with observed transition from intergranular fracture to cleavage. Carbon follows the moving crack tip through repeated breaking and reforming of Fe–C bonds, enriching the crack-tip region. We term this process dynamic bond reconfiguration (DBR). DBR dynamically amplifies static cohesive strengthening, revealing dynamic solute–crack-tip interactions as a framework for controlling brittle fracture in alloys.

查看原文本刊更多论文

动态键重配置驱动裂纹尖端碳迁移，强化铁晶界

开发更强的合金对于结构应用至关重要，但它们的可靠性受到沿一般晶界（GGBs）脆裂的限制。溶质偏析可以控制晶界断裂，但其作用通常用界面分离前后的静态黏聚变化来解释。微量溶质如何改变ggb的抗移动裂纹性能尚不清楚，因为实际的偏析和裂纹扩展很难从原子上处理。在这里，我们开发了一个快速和可转移的Fe-C机器学习原子间势。通过模拟实验一致的碳分离α-Fe ggb，我们发现微量碳显著增加了抗裂纹扩展能力，这与观察到的从晶间断裂到解理的转变一致。碳随着裂纹尖端的移动，通过Fe-C键的反复断裂和重整，丰富了裂纹尖端区域。我们称这一过程为动态键重构（DBR）。DBR动态地放大了静态内聚强化，揭示了作为控制合金脆性断裂框架的动态溶质-裂纹尖端相互作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

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.