Collaborative Research: DMREF: Microstructure by Design: Integrating Grain Growth Experiments, Data Analytics, Simulation, and Theory

合作研究：DMREF：微观结构设计：整合晶粒生长实验、数据分析、模拟和理论

• 批准号: 2118206
• 负责人: Katayun Barmak
• 金额: $ 72.46万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118206&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Microstructure; Design

Most technologically useful materials are polycrystalline microstructures composed of a myriad of small monocrystalline grains delimited by grain boundaries. An understanding of the evolution of grain boundaries and associated grain growth (coarsening) is essential in determining the properties of materials across multiple scales. Despite tremendous progress in formulating microstructural models, however, current descriptions do not fully account for various grain growth mechanisms, detailed grain topologies and the effects of different time scales on microstructural evolution. As a result, conventional theories have limited predictive capability. The goal of the project is to develop a predictive theory of grain growth in polycrystalline materials through the construction of novel, closely integrated data-driven numerical simulation and mathematical modeling combined with data analytics, analysis, and a set of critical experiments. This interdisciplinary project, requiring the complementary expertise of applied mathematicians and materials scientists, is firmly aligned with the Materials Genome Initiative. The new knowledge and tools that will emerge from the project will have a profound impact on the performance and reliability of polycrystalline materials used in many technologically useful systems and structures, thereby expediting advanced materials development and deployment. Predictive computational algorithms and data will be made available and accessible to other researchers. For the training of the next-generation materials workforce, in addition to mentoring of graduate and undergraduate students, the PIs (from Columbia University, Illinois Institute of Technology, Lehigh University and University of Utah) will participate in outreach activities and will continue to work towards increasing diversity and broadening participation within STEM.Grain growth is a very complex process and may be viewed as the anisotropic evolution of a large metastable network. One of the main thrusts of the project will be to uncover possible stochastic processes that define the evolution of various statistical measures of grain growth, discover relations among them, and establish links to materials properties. Results from structure-preserving numerical simulations alongside critical sets of experiments and new experimental data will be invaluable in navigating the modeling and analysis. The project will also create and employ specific data analysis techniques for the study of dynamic evolution of grains in experimental and computational systems with the goal of validating and further refining the microstructural models. This component of the project, will lead to a) the development of new materials informatics methods, b) innovative stochastic differential equations/differential equations models of grain growth, c) new mathematical and numerical analysis techniques for coarsening systems, as well as d) improved computational tools. In turn, the results of combined data analytics, modeling and analysis will be used to guide the design of subsequent experiments. Experimentally, grain growth will be examined in prototypical metallic thin films (Pd, Ni, Cr, Fe). As most elemental metals and many metallic alloys have cubic structures, the proposed studies will have broad applicability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

最有用的材料是多晶微观结构，该微结构由晶界界定的无数小单晶晶粒组成。了解晶界的演变和相关的晶粒生长（粗化）对于确定跨多个尺度的材料的性质至关重要。尽管在制定微观结构模型方面取得了巨大进展，但是当前的描述并未充分说明各种谷物生长机制，详细的晶粒拓扑以及不同时间尺度对微结构进化的影响。结果，常规理论的预测能力有限。该项目的目的是通过构建新颖的，紧密整合的数据驱动的数值模拟和数学建模，结合了数据分析，分析和一组关键实验，来开发多晶材料中晶粒生长的预测理论。这个跨学科的项目需要应用数学家和材料科学家的互补专业知识，与材料基因组计划完全一致。该项目将出现的新知识和工具将对许多技术有用的系统和结构中使用的多晶材料的性能和可靠性产生深远的影响，从而加快了先进的材料开发和部署。 其他研究人员将提供预测性计算算法和数据。为了培训下一代材料劳动力，除了指导研究生和本科生外，PI（来自哥伦比亚大学，伊利诺伊州伊利诺伊大学，利哈伊大学和犹他大学研究所）还将继续参加外展活动，并将继续努力努力增加茎中的多样性和扩大的参与。该项目的主要目的之一是揭示可能定义谷物生长各种统计测量的随机过程，发现它们之间的关系并建立与材料特性的联系。结构保存数值模拟以及临界实验集和新实验数据的结果对于导航建模和分析将是无价的。该项目还将创建并采用特定的数据分析技术来研究实验和计算系统中晶粒动态演变的研究，以验证和进一步完善微观结构模型。该项目的这一组成部分将导致a）开发新材料信息学方法，b）创新的随机微分方程/差异方程谷物生长模型，c）用于变高系统的新数学和数值分析技术，以及d）改进了计算工具。 反过来，组合数据分析，建模和分析的结果将用于指导后续实验的设计。在实验上，将在原型金属薄膜（PD，NI，CR，FE）中检查晶粒生长。由于大多数元素金属和许多金属合金具有立方结构，因此拟议的研究将具有广泛的适用性。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来提供支持。

项目成果

Relative grain boundary energies from triple junction geometry: Limitations to assuming the Herring condition in nanocrystalline thin films

三结几何形状的相对晶界能量：假设纳米晶薄膜中赫林条件的局限性

• DOI: 10.1016/j.actamat.2022.118476
• 发表时间: 2023
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Patrick, Matthew J.;Rohrer, Gregory S.;Chirayutthanasak, Ooraphan;Ratanaphan, Sutatch;Homer, Eric R.;Hart, Gus L． W.;Epshteyn, Yekaterina;Barmak, Katayun
• 通讯作者: Barmak, Katayun