DMREF: Accelerating the Design of Adhesives with Nanoscale Control of Thermomechanical Properties

该设计材料变革和工程我们的未来（DMREF）资助支持产生与先进粘合剂设计相关的新知识，用于许多行业，包括汽车，航空航天，电子，建筑和国防。与传统紧固件相比，粘合剂具有显著的优势，因为它们可以将不同的材料相互结合，并且可以应用于广泛的区域，从而分布施加的应力并减少失效。然而，传统的环氧胶粘剂可能非常脆。该研究项目将专注于了解环氧胶粘剂的结构-性能关系，从而设计出更不脆、更环保、更轻量的胶粘剂。这项工作将结合最先进的实验、模拟和机器学习技术来了解这些结构粘合剂，并加快设计和开发周期。作为这一方法的一部分，我们将确保数据和模型将被广泛共享，以便学术界、工业界和政府研究人员能够利用它们。社会将从这个项目中受益，不仅通过结构粘合剂的新知识，还通过三所大学的不同学生群体的教育和培训，了解如何有效地将实验和建模方法结合起来设计新材料。分层结构的胶粘剂表现出特殊的希望，将多种增韧机制纳入环氧胶粘剂中。然而，关于这些材料的潜在多尺度组装和增韧机制还有许多悬而未决的问题。层次化环氧胶粘剂的组合变量空间大，多尺度结构和性能复杂，在多性能优化过程中存在固有的权衡问题，开发周期长达数十年。这项工作的主要目标是发展必要的基本理解，以便发现和设计具有定制热机械性能的分层有序粘合剂。具体来说，我们将开发经过验证的多尺度模型，以增加我们对嵌段共聚物在环氧树脂中的自组装、附着力和等级有序粘合剂的其他关键机械性能（韧性、模量）的理解。材料的分层结构将使用自上而下/自下而上的组合方法开发，分别利用增材制造和嵌段共聚物自组装。为了加速发现过程，实验和多尺度建模生成的数据将与用于属性预测的机器学习工具一起合并到使用机器学习辅助属性筛选的现有工具聚合物设计中。由此产生的升级工具将是一个集成的计算材料工程框架，用于粘合剂设计，与分子构建模块和定量结构-性能-性能关系有很强的联系，将提供给更广泛的利益相关者社区。该项目由工程理事会（ENG）的土木、机械和制造创新司（CMMI）以及数学和物理科学理事会（MPS）的材料研究司（DMR）支持。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。

This Designing Materials to Revolutionize and Engineer our Future (DMREF) grant supports the generation of new knowledge related to the design of advanced adhesives for use in many industries, including automotive, aerospace, electronics, construction, and defense. Adhesives have significant benefits over traditional fasteners because they can bind different materials to one another and can be applied to wide areas which distributes the applied stresses and reduces failure. However, traditional epoxy adhesives can be very brittle. This research project will focus on understanding the structure-property relationships of epoxy adhesives that will enable the design of adhesives that are less brittle, more environmentally friendly, and are lightweight. This work will combine state-of-the-art experimental, simulation, and machine learning techniques to learn about these structured adhesives and accelerate the design and development cycles. As a part of this approach, we will ensure that the data and models will be widely shared so that they can be leveraged by academic, industrial and government researchers. Society will benefit from this project both through the new knowledge of structured adhesives, as well as through the education and training of diverse student populations at three universities on how to effectively combine experimental and modeling approaches to design new materials. Hierarchically structured adhesives exhibit exceptional promise to incorporate multiple toughening mechanisms into epoxy adhesives. However, there are many outstanding questions about the potential multiscale assembly and toughening mechanisms of these materials. Hierarchical epoxy adhesives have large combinatorial variable spaces, complicated multiscale structures and properties, and inherent tradeoffs during multi-property optimization, which results in decades-long development cycles. The primary objective of this work is to develop the requisite fundamental understanding to enable the discovery and design of hierarchically ordered adhesives with tailored thermomechanical properties. Specifically, we will develop validated multiscale models to increase our understanding of block copolymer self-assembly in epoxy, the adhesion forces, and other key mechanical properties (toughness, modulus) for hierarchically ordered adhesives. The hierarchical structure of the materials will be developed using a combined top-down/bottom-up approach which leverages additive manufacturing and block copolymer self-assembly, respectively. To accelerate the discovery process, experimental and multiscale modeling-generated data will be incorporated along with machine learning tools for property prediction into an existing tool called Polymer design using Machine-learning Assisted Property Screening. The resultant upgraded tool will be an integrated computational materials engineering framework for adhesive design with strong connections to molecular building blocks and quantitative structure-property-performance relations which will be made available to a broader community of stakeholders.This project is supported by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG), and the Division of Materials Research (DMR) of the Directorate for Mathematical and Physical Sciences (MPS).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.