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

该奖项支持将为有效发现新材料系统贡献知识的研究。 预计新材料将在从生物医学系统到基础设施和能源系统的广泛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，因为它主要依赖于物理测试和实验。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靠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材料的新信息，并有效地指导材料设计工作，使其具有所需和有价值的特性。该研究预计将大大加快材料设计过程，为美国工业带来显著的竞争优势。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以开源方式共享，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、研究人员和从业人员。 这个项目的主要研究目标是批判性地评估不同的建模形式主义和方法的相对优点，捕捉和利用材料领域的知识的方式，是最有价值的设计师。在设计过程中，将结合多种信息来源，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-属性模型。 该假设是，从一个投资组合的信息源与协同成本准确性权衡相结合的信息，导致一个更有效的设计过程。第二个重点是将这些来源的信息整合到整合的降阶过程-结构-属性联系中。 这些联系支持学习，通过贝叶斯更新新的信息被收购，他们是计算成本低廉，因此非常适合搜索的设计空间。 该奖项反映了NSF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。

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.