Collaborative Research: QRM: Microstructure Manifold Analysis Using Hierarchical Set of Morphological, Topological, and Process Descriptors

结构材料的性质和性能由其微观结构决定，称为微观结构。先进的制造工艺寻求调整微观结构，以获得具有所需性能的材料。实现这种制造工艺的一个关键要求是定量描述微结构的能力。 该奖项支持数学工具的开发，以量化形状方面的材料微观结构（即，形态学），几何学和连通性（即，拓扑），和属性（即，在统一和一致的框架内。这种表征微观结构的定量方法用于了解不同的制造途径如何影响微观结构及其性能。有效和自适应探索的研究技术确保了这种处理属性景观的实验数量减少。虽然该项目旨在奠定微观结构量化的基础，但该研究有可能在几个应用领域推进知识，如有机电子，电池和燃料电池的多孔电极以及膜。这项研究有可能通过降低工程介观结构敏感材料的成本和上市时间来加速具有上级性能的新器件的设计。因此，这项工作将有助于提高国家制造业部门的全球竞争力。该项目的教育和劳动力发展方面涉及培养下一代具有全球竞争力的工程师和科学家。该项目的总体目标是为使用一套全面的描述符和增量降维技术奠定基础，以适应性和有效地建立可靠的加工-结构-性能关系。一套全面的拓扑，形态和几何描述符将被用作标记，以适应性地学习微观结构流形使用现代增量流形学习策略。工艺-结构-性能关系可以通过使用这种微结构流形自然地参数化和探索。该奖项将提供有关计算和数据驱动材料科学的教育模块。合作机构的各种现有机制将被利用来推进少数民族和妇女招聘，本科生研究和K-12推广的目标。该奖项反映了NSF的法定使命，并被认为值得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来支持。

The properties and performance of structural materials are dictated by their structure on the microscopic level, referred to as the microstructure. Advanced manufacturing processes seek to tune the microstructure to obtain materials with desired properties. One key requirement to enable such manufacturing processes is the ability to quantitatively describe the microstructures. This award supports the development of mathematical tools to quantify the material microstructure in terms of shape (i.e., morphology), geometry and connectedness (i.e., topology), and property (i.e., performance) within a unified and consistent framework. Such a quantitative approach to characterizing microstructures is used to understand how different manufacturing pathways affect the microstructure and its properties. Researched techniques for efficient and adaptive exploration ensure that this processing-property landscape is explored with a reduced number of experiments. Although this project is geared towards laying the foundations of microstructure quantification, the research has the potential to advance knowledge in several application areas, such as organic electronics, porous electrodes for batteries and fuel cells, and membranes. This research has the potential to accelerate the design of new devices with superior properties by reducing the cost and time-to-market for engineered mesostructure-sensitive materials. Therefore, this work will contribute to enhancing the global competitiveness of the national manufacturing sector. The education and workforce development aspects of the project involve training of the next generation of globally competitive engineers and scientists.The overarching goal of this project is to lay the foundations for using a comprehensive suite of descriptors with incremental dimensionality reduction techniques to adaptively and efficiently build reliable processing-structure-property relationships. A comprehensive suite of topological, morphological and geometric descriptors will be used as markers to adaptively learn the microstructure manifold using modern incremental manifold learning strategies. Process-structure-property relationships can then be naturally parametrized and explored by using this microstructure manifold. This award will provide educational modules about computational and data-driven materials science. Diverse existing mechanisms at the partner institutions will be leveraged to advance goals of minority and women recruitment, undergraduate research, and K-12 outreach.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.