{"title":"有限晶界结迁移率数据驱动估计的相场框架","authors":"Eisuke Miyoshi , Akinori Yamanaka","doi":"10.1016/j.commatsci.2025.114161","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents a novel framework for estimating the finite mobilities of grain boundary junctions, which are difficult to measure using conventional experiments and calculations, by coupling phase-field modeling with Bayesian data assimilation. We propose a multi-phase-field model that effectively represents finite mobilities of both triple and quadruple junctions using dimensionless parameters. For triple junctions, we establish a quantitative correlation between the model parameter and physical junction mobility. The ensemble Kalman filter-based data assimilation is incorporated into the developed multi-phase-field model to estimate multiple grain boundary and junction mobilities from grain growth observations. Through numerical testing using synthetic observation data of polycrystalline grain growth, we demonstrate that the framework can accurately estimate wide-ranging mobility values with relative errors of less than 3%, even for systems with strong nonuniformity in junction mobilities. The proposed framework enables simultaneous estimation of the grain boundary and junction mobilities from typical grain growth observation, overcoming the limitations of conventional measurement methods that require specifically designed specimens. This work offers a promising method for the quantitative characterization of junction mobilities, which is crucial for the understanding of microstructural evolution in nanocrystals or sintered particles where the traditional assumption of infinite junction mobility is not valid.</div></div>","PeriodicalId":10650,"journal":{"name":"Computational Materials Science","volume":"259 ","pages":"Article 114161"},"PeriodicalIF":3.3000,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Phase-field framework for data-driven estimation of finite grain boundary junction mobilities\",\"authors\":\"Eisuke Miyoshi , Akinori Yamanaka\",\"doi\":\"10.1016/j.commatsci.2025.114161\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents a novel framework for estimating the finite mobilities of grain boundary junctions, which are difficult to measure using conventional experiments and calculations, by coupling phase-field modeling with Bayesian data assimilation. We propose a multi-phase-field model that effectively represents finite mobilities of both triple and quadruple junctions using dimensionless parameters. For triple junctions, we establish a quantitative correlation between the model parameter and physical junction mobility. The ensemble Kalman filter-based data assimilation is incorporated into the developed multi-phase-field model to estimate multiple grain boundary and junction mobilities from grain growth observations. Through numerical testing using synthetic observation data of polycrystalline grain growth, we demonstrate that the framework can accurately estimate wide-ranging mobility values with relative errors of less than 3%, even for systems with strong nonuniformity in junction mobilities. The proposed framework enables simultaneous estimation of the grain boundary and junction mobilities from typical grain growth observation, overcoming the limitations of conventional measurement methods that require specifically designed specimens. This work offers a promising method for the quantitative characterization of junction mobilities, which is crucial for the understanding of microstructural evolution in nanocrystals or sintered particles where the traditional assumption of infinite junction mobility is not valid.</div></div>\",\"PeriodicalId\":10650,\"journal\":{\"name\":\"Computational Materials Science\",\"volume\":\"259 \",\"pages\":\"Article 114161\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Materials Science\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S092702562500504X\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Materials Science","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S092702562500504X","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Phase-field framework for data-driven estimation of finite grain boundary junction mobilities
This paper presents a novel framework for estimating the finite mobilities of grain boundary junctions, which are difficult to measure using conventional experiments and calculations, by coupling phase-field modeling with Bayesian data assimilation. We propose a multi-phase-field model that effectively represents finite mobilities of both triple and quadruple junctions using dimensionless parameters. For triple junctions, we establish a quantitative correlation between the model parameter and physical junction mobility. The ensemble Kalman filter-based data assimilation is incorporated into the developed multi-phase-field model to estimate multiple grain boundary and junction mobilities from grain growth observations. Through numerical testing using synthetic observation data of polycrystalline grain growth, we demonstrate that the framework can accurately estimate wide-ranging mobility values with relative errors of less than 3%, even for systems with strong nonuniformity in junction mobilities. The proposed framework enables simultaneous estimation of the grain boundary and junction mobilities from typical grain growth observation, overcoming the limitations of conventional measurement methods that require specifically designed specimens. This work offers a promising method for the quantitative characterization of junction mobilities, which is crucial for the understanding of microstructural evolution in nanocrystals or sintered particles where the traditional assumption of infinite junction mobility is not valid.
期刊介绍:
The goal of Computational Materials Science is to report on results that provide new or unique insights into, or significantly expand our understanding of, the properties of materials or phenomena associated with their design, synthesis, processing, characterization, and utilization. To be relevant to the journal, the results should be applied or applicable to specific material systems that are discussed within the submission.