Collaborative Research: Towards a Predictive Theory of Microstructure Evolution in Polycrystalline Materials

合作研究：多晶材料微观结构演化的预测理论

• 批准号: 1905492
• 负责人: Katayun Barmak
• 金额: $ 45万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1905492&HistoricalAwards=false
• 关键词: Collaborative; Research; Towards; Predictive; Theory

Most technologically useful materials - spanning the length scale from meters to nanometers, from aircraft to microprocessors - are polycrystalline. Crystals are materials with ordered arrangements of atoms. Polycrystalline microstructures are composed of a myriad of small monocrystalline cells/grains separated by grain boundaries/interfaces. Grain boundaries play a crucial role in determining the properties of materials across a wide range of scales. These properties include mechanical strength and ductility, electrical resistivity, magnetic hardness, etc.; they strongly impact the performance of materials in engineered systems. A grand challenge problem in the engineering of polycrystals is to develop prescriptive manufacturing process technologies capable of producing an arrangement of grains that yields a desired set of materials properties. One method by which the grain structure is engineered is grain growth or coarsening of a starting structure. This project is aimed at developing a predictive theory of grain growth through close integration of experiments, simulations, and mathematical models. The project will involve interdisciplinary research and will enhance the infrastructure of engineered materials and systems through the development of new, predictive and prescriptive experimental, analytical and computational tools that will help in the design of material microstructures with predictable properties. The new knowledge and tools that will emerge from the proposed program will have an impact on the performance and reliability of polycrystalline materials used in engineered systems. This project will also directly impact workforce development through training and education of graduate and undergraduate students in the proposed research. In addition, the investigators will engage in outreach activities that include training of underrepresented groups in STEM.Grain growth can be viewed as the evolution of a large metastable network, and can be mathematically modeled by a set of deterministic local evolution laws for the growth of an individual grain combined with stochastic models to describe the interaction between them. Hence, to develop a predictive theory, a broad range of statistical measures for microstructure evolution during grain growth will be investigated using experiments, simulation, and mathematical modeling. The main goal of this effort will be to identify/derive possible stochastic processes that drive the evolution of various statistical measures, understand possible links between them, and establish connections to materials properties. As a part of the project, tools from mathematical analysis, partial differential equations, statistics, scientific computing, numerical analysis and high-performance computing will be closely integrated with experimental data and experiments. The convergence of experiments, numerical simulation and mathematical modeling through an integrated synergistic approach is the hallmark of the proposed program, and it is essential in order to improve upon existing models of grain growth and guide the design of new experiments.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.

大多数技术上有用的材料——从米到纳米，从飞机到微处理器——都是多晶材料。晶体是原子有序排列的物质。多晶微结构是由无数由晶界/界面分隔的小单晶细胞/晶粒组成的。晶界在很大范围内决定材料的性能方面起着至关重要的作用。这些性能包括机械强度和延展性、电阻率、磁硬度等；它们强烈影响工程系统中材料的性能。多晶体工程中的一个重大挑战问题是开发规范的制造工艺技术，能够生产出一种能够产生所需材料性能的颗粒排列。设计晶粒结构的一种方法是晶粒生长或初始组织的粗化。该项目旨在通过实验、模拟和数学模型的紧密结合，发展一种预测谷物生长的理论。该项目将涉及跨学科研究，并将通过开发新的、预测性和规范性的实验、分析和计算工具来增强工程材料和系统的基础设施，这些工具将有助于设计具有可预测特性的材料微观结构。新知识和工具将从拟议的计划中出现，将对工程系统中使用的多晶材料的性能和可靠性产生影响。这个项目也将直接影响劳动力的发展，通过培训和教育研究生和本科生在拟议的研究。此外，调查人员将参与外展活动，包括对STEM中代表性不足的群体进行培训。晶粒的生长可以看作是一个大的亚稳网络的演化，可以用一组单个晶粒生长的确定性局部演化规律结合描述它们之间相互作用的随机模型来进行数学建模。因此，为了发展预测理论，将使用实验、模拟和数学建模来研究晶粒生长过程中微观结构演变的广泛统计测量。这项工作的主要目标将是识别/推导可能的随机过程，这些随机过程驱动各种统计测量的演变，理解它们之间可能的联系，并建立与材料特性的联系。作为项目的一部分，数学分析、偏微分方程、统计学、科学计算、数值分析和高性能计算等工具将与实验数据和实验紧密结合。通过综合的协同方法将实验、数值模拟和数学建模结合起来是本项目的特点，这对于改进现有的晶粒生长模型和指导新实验的设计至关重要。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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