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.

结构材料的特性和性能由其微观结构（称为微观结构）决定。先进的制造工艺旨在调整微观结构以获得具有所需性能的材料。实现此类制造工艺的一项关键要求是能够定量描述微观结构。 该奖项支持数学工具的开发，以在统一一致的框架内根据形状（即形态）、几何和连通性（即拓扑）以及属性（即性能）来量化材料微观结构。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。高效和适应性探索的研究技术确保通过减少实验次数来探索这种加工特性景观。尽管该项目旨在为微观结构量化奠定基础，但该研究有潜力推进多个应用领域的知识，例如有机电子、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卓越性能的新设备的设计。因此，这项工作将有助于提升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来自适应和高效地构建基础奠定基础。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。然后可以通过使用这种微观结构流形自然地参数化和探索过程-结构-性能关系。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用来推进少数族裔和女性招募、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，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。

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: 2022-02
• 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

用于有机电子结构-性能映射的多保真机器学习模型

• DOI: 10.1016/j.commatsci.2022.111599
• 发表时间: 2022-10-01
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Chih;B. Pokuri;Xian Yeow Lee;S. Balakrishnan;C. Hegde;S. Sarkar;B. Ganapathysubramanian
• 通讯作者: B. Ganapathysubramanian

A graph based approach to model charge transport in semiconducting polymers

基于图的方法来模拟半导体聚合物中的电荷传输

• DOI: 10.1038/s41524-022-00714-w
• 发表时间: 2022-03
• 期刊: npj Computational Materials
• 影响因子: 9.7
• 作者: Noruzi, Ramin;Lim, Eunhee;Pokuri, Balaji Sesha Sarath;Chabinyc, Michael L.;Ganapathysubramanian, Baskar
• 通讯作者: Ganapathysubramanian, Baskar

Nanoscale control of internal inhomogeneity enhances water transport in desalination membranes

内部不均匀性的纳米级控制增强了海水淡化膜中的水传输

• DOI: 10.1126/science.abb8518
• 发表时间: 2020-12
• 期刊: Science
• 影响因子: 56.9
• 作者: Culp, Tyler E.;Khara, Biswajit;Brickey, Kaitlyn P.;Geitner, Michael;Zimudzi, Tawanda J.;Wilbur, Jeffrey D.;Jons, Steven D.;Roy, Abhishek;Paul, Mou;Ganapathysubramanian, Baskar;et al
• 通讯作者: et al

GraSPI: Extensible software for the graph-based quantification of morphology in organic electronics

GraSPI：用于有机电子学中基于图形的形态量化的可扩展软件

• DOI: 10.1016/j.softx.2021.100969
• 发表时间: 2022-01
• 期刊: SoftwareX
• 影响因子: 3.4
• 作者: Jivani, Devyani;Zola, Jaroslaw;Ganapathysubramanian, Baskar;Wodo, Olga
• 通讯作者: Wodo, Olga