Collaborative Research: Efficient Learning of Process-Structure-Property Models in Value-Driven Materials Design

协作研究：价值驱动材料设计中过程-结构-性能模型的有效学习

基本信息

批准号：
1761406
负责人：
Surya Kalidindi
金额：
$ 34.36万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2021-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761406&HistoricalAwards=false
关键词：
Collaborative Research Efficient Learning Process

项目摘要

This award supports research that will contribute knowledge towards the efficient discovery of new material systems. New materials are expected to have a significant impact in a broad range of application domains, ranging from biomedical systems to infrastructure and energy systems, improving the efficiencies of these systems and creating new capabilities that have so far been technologically out of reach. In current practice, however, the development of new materials is very costly and time-consuming because it relies mostly on physical testing and experimentation. Rather than focusing on the development of a specific new material, this award aims to develop modeling approaches and learning algorithms that allow for more efficient and effective exploration and discovery of new materials in general. The results of the investigation will help material scientists and engineers understand when to rely on mathematical analysis models or when to use physical experiments so that new information about so far unexplored materials can be gathered efficiently, and so that the materials design effort can be efficiently guided towards materials with desired and valuable properties. The research is expected to lead to a dramatic acceleration of the materials design process with significant competitive advantages to US industry. Through a university-industry consortium these innovations will be transferred into industrial practice. All new models and algorithms will be shared open-source, and the research findings, methods and tools will be incorporated in on-campus and on-line courses, with the potential to reach a large number of students, researchers and practitioners. The main research objective of this project is to critically evaluate the relative merits of different modeling formalisms and approaches for capturing and utilizing materials domain knowledge in a way that is most valuable to the designer. In the design process, multiple information sources will be combined, including bulk material tests, low-cost experimental assays, and physics-based multiscale Process-Structure-Property models. The hypothesis is that combining information from a portfolio of information sources with synergistic cost-accuracy trade-offs leads to a more efficient and effective design process. A second focus is on combining the information from these sources into integrative reduced-order Process-Structure-Property linkages. These linkages support learning through Bayesian updating as new information is acquired, and they are computationally inexpensive and therefore well-suited for searching the design space. The overall design framework will be applied and validated in the context of dual phase steels.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.

该奖项支持为有效发现新材料系统贡献知识的研究。新材料预计将在从生物医学系统到基础设施和能源系统等广泛的应用领域产生重大影响，提高这些系统的效率并创造迄今为止技术上无法实现的新功能。然而，在目前的实践中，新材料的开发非常昂贵且耗时，因为它主要依赖于物理测试和实验。该奖项的目的不是专注于特定新材料的开发，而是旨在开发建模方法和学习算法，以便更有效地探索和发现新材料。研究结果将帮助材料科学家和工程师了解何时依赖数学分析模型或何时使用物理实验，以便有效收集迄今为止未探索的材料的新信息，从而有效地指导材料设计工作寻找具有所需和有价值特性的材料。该研究预计将显着加速材料设计过程，为美国工业带来显着的竞争优势。通过大学-工业联盟，这些创新将转化为工业实践。所有新的模型和算法都将开源共享，研究成果、方法和工具将纳入校园和在线课程，有可能惠及大量学生、研究人员和从业者。该项目的主要研究目标是批判性地评估不同建模形式的相对优点以及以对设计师最有价值的方式捕获和利用材料领域知识的方法。在设计过程中，将结合多种信息源，包括散装材料测试、低成本实验分析和基于物理的多尺度过程-结构-性能模型。假设将来自信息源组合的信息与协同成本准确性权衡相结合，可以实现更高效、更有效的设计过程。第二个重点是将来自这些来源的信息组合成集成的降阶过程-结构-属性链接。这些链接支持在获取新信息时通过贝叶斯更新进行学习，并且它们的计算成本低廉，因此非常适合搜索设计空间。整体设计框架将在双相钢的背景下应用和验证。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。