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: 2118197
• 负责人: Jeffrey Rickman
• 金额: $ 38.99万
• 依托单位: Lehigh 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 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118197&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.

大多数技术上有用的材料是由无数由晶界界定的小单晶颗粒组成的多晶微观结构。了解晶界的演变和相关的晶粒生长（粗化）对于确定多尺度材料的性能至关重要。然而，尽管在制定微观结构模型方面取得了巨大进展，但目前的描述并没有完全解释各种晶粒生长机制、详细的晶粒拓扑以及不同时间尺度对微观结构演化的影响。因此，传统理论的预测能力有限。该项目的目标是通过构建新颖、紧密集成的数据驱动数值模拟和数学建模，并结合数据分析、分析和一系列关键实验，开发多晶材料晶粒生长的预测理论。这个跨学科项目需要应用数学家和材料科学家的专业知识互补，与材料基因组计划紧密结合。该项目将产生的新知识和工具将对许多技术上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，从而加快先进材料的开发和部署。 预测计算算法和数据将可供其他研究人员使用。为了培养下一代材料劳动力，除了对研究生和本科生进行指导外，PI（来自哥伦比亚大学、伊利诺伊理工学院、利哈伊大学和犹他大学）将参加外展活动并继续工作晶粒生长是一个非常复杂的过程，可以被视为大型亚稳态网络的各向异性演化。该项目的主要目标之一是揭示可能的随机过程，这些过程定义了晶粒生长的各种统计测量的演变，发现它们之间的关系，并建立与材料特性的联系。保留结构的数值模拟的结果以及关键的实验集和新的实验数据对于建模和分析将具有无价的价值。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颗粒的动态演化，以验证和进一步完善微观结构模型。该项目的这一部分将导致a）新材料信息学方法的开发，b）创新的随机微分方程/晶粒生长的微分方程模型，c）用于粗化系统的新数学和数值分析技术，以及d）改进的计算工具。 反过来，数据分析、建模和分析相结合的结果将用于指导后续实验的设计。通过实验，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都具有立方结构，因此拟议的研究将具有广泛的适用性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。