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是由其微观结构决定的，我们称之为微观结构。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能够定量描述微结构。该奖项支持开发数学工具，在统一和一致的框架内根据形状(即形态)、几何和连通性(即拓扑结构)和性能(即性能)来量化材料的微观结构。这种表征微结构的定量方法被用来理解不同的制造路径如何影响微结构及其性能。研究的高效和适应性勘探技术确保了以较少的实验次数探索这一加工属性景观。尽管该项目旨在为微观结构量化奠定基础，但该研究有可能在几个应用领域推进知识，如有机电子学、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设计具有优异性能的新设备。因此，这项工作将有助于增强国家制造业的全球竞争力。该项目的教育和劳动力发展方面涉及培训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具有增量降维技术的全面描述符套件奠定基础，以自适应和有效地建立可靠的工艺-结构-性能关系。将使用一套全面的拓扑、形态和几何描述符作为标记，使用现代增量流形学习策略自适应地学习微结构流形。然后，工艺-结构-性能关系可以自然地参数化，并通过使用这种微结构流形来探索。该奖项将提供有关计算和数据驱动的材料科学的教育模块。合作机构的各种现有机制将被用来推进少数族裔和女性招聘、本科研究和K-12外展的目标。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.