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

合作研究：QRM：使用形态、拓扑和过程描述符的分层集进行微观结构流形分析

• 批准号: 1906194
• 负责人: Baskar Ganapathysubramanian
• 金额: $ 23.1万
• 依托单位: Iowa State 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: 2019-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1906194&HistoricalAwards=false
• 关键词: Collaborative; Research; QRM; Microstructure; Manifold

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.

结构材料的性质和性能是由它们在微观层面上的结构决定的，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能够定量地描述微观结构。该奖项支持数学工具的开发，以在统一和一致的框架内量化材料的形状（即形态）、几何形状和连通性（即拓扑）以及性能（即性能）方面的微观结构。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。研究了有效和自适应的勘探技术，确保了这种加工属性景观的探索与减少实验次数。虽然该项目旨在为微观结构量化奠定基础，但该研究具有在有机电子学、电池和燃料电池的多孔电极以及膜等多个应用领域推进知识的潜力。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优异性能的新设备的设计。因此，这项工作将有助于提高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一套全面的描述符和增量降维技术奠定基础，以自适应和有效地构建可靠的处理-结构-属性关系。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观结构流形。然后，利用该微结构流形可以自然地对工艺-结构-性能关系进行参数化和探索。该奖项将提供有关计算和数据驱动材料科学的教育模块。将利用合作院校的各种现有机制来推进少数族裔和女性招生、本科研究和K-12推广等目标。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

How important is microstructural feature selection for data-driven structure-property mapping?

微观结构特征选择对于数据驱动的结构-性能映射有多重要？

• DOI: 10.1557/s43579-021-00147-4
• 发表时间: 2022
• 期刊: MRS Communications
• 影响因子: 1.9
• 作者: Liu, Hao;Yucel, Berkay;Wheeler, Daniel;Ganapathysubramanian, Baskar;Kalidindi, Surya R.;Wodo, Olga
• 通讯作者: Wodo, Olga

Multi-fidelity machine learning models for structure–property mapping of organic electronics

• DOI: 10.1016/j.commatsci.2022.111599
• 发表时间: 2022-10
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Chih-Hsuan Yang;B. Pokuri;Xian Yeow Lee;S. Balakrishnan;C. Hegde;S. Sarkar;B. Ganapathysubramanian-B.-Ganapathysubramani

A graph based approach to model charge transport in semiconducting polymers

• DOI: 10.1038/s41524-022-00714-w
• 发表时间: 2022-03-11
• 期刊: NPJ COMPUTATIONAL MATERIALS
• 影响因子: 9.7
• 作者: Noruzi, Ramin;Lim, Eunhee;Ganapathysubramanian, Baskar
• 通讯作者: Ganapathysubramanian, Baskar

Nanoscale control of internal inhomogeneity enhances water transport in desalination membranes

• DOI: 10.1126/science.abb8518
• 发表时间: 2021-01-01
• 期刊: SCIENCE
• 影响因子: 56.9
• 作者: Culp, Tyler E.;Khara, Biswajit;Gomez, Enrique D.
• 通讯作者: Gomez, Enrique D.

Feature Engineering for Microstructure–Property Mapping in Organic Photovoltaics

• DOI: 10.1007/s40192-022-00267-2
• 发表时间: 2021-11
• 期刊: Integrating Materials and Manufacturing Innovation
• 影响因子: 3.3
• 作者: S. Hashemi;B. Ganapathysubramanian;S. Casey;Jie Su;S. Kalidindi