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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.

该奖项支持将为有效发现新材料系统贡献知识的研究。预计新材料将在广泛的应用领域产生重大影响，从生物医学系统到基础设施和能源系统，提高这些系统的效率，创造迄今在技术上遥不可及的新能力。然而，在目前的实践中，新材料的开发非常昂贵和耗时，因为它主要依赖物理测试和实验。该奖项不是专注于开发特定的新材料，而是旨在开发建模方法和学习算法，以允许更有效地探索和发现一般的新材料。调查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，以便有效地收集有关迄今未探索的材料的新信息，从而有效地指导材料设计工作，使材料具有所需和有价值的性能。预计这项研究将导致材料设计过程的显著加快，对美国工业具有显著的竞争优势。通过一个大学-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将被开源共享，研究成果、方法和工具将被纳入校园和在线课程，有可能接触到大量的学生、研究人员和实践者。这个项目的主要研究目标是批判性地评估不同建模形式和方法的相对优点，以便以对设计师最有价值的方式获取和利用材料领域知识。在设计过程中，将结合多种信息源，包括大宗材料测试、低成本实验分析和基于物理的多尺度工艺-结构-性能模型。这个假设是，将来自信息源组合的信息与协同成本-精度权衡结合起来，可以产生更高效和更有效的设计过程。第二个重点是将来自这些来源的信息合并成综合的降阶过程--结构--性质联系。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较低，因此非常适合搜索设计空间。总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优势和更广泛的影响审查标准进行评估，被认为值得支持。