DMREF/Collaborative Research: Controlling Hierarchical Nanostructures in Conjugated Polymers

非技术概述有机聚合物在现代日常生活中无处不在，它们使从医疗保健到计算机技术等领域的进步成为可能。 聚合物的最大潜力在于其多功能性。 例如，最近的系统已经被设计成导电。 导电聚合物是潜在的游戏规则改变者，因为它们可以溶解在溶剂中，并像墨水一样印刷在低成本电子产品和传感器的柔性基底上。 使用导电聚合物制造电子器件需要能够预测印刷工艺如何影响其性能。 该项目的PI和学生将使用计算方法指导设计并加速高性能导电聚合物的发现的方法开发新材料。 该项目对未来的科学和工程劳动力具有重大影响，研究生研究人员将获得计算机模拟和物理实验相结合的关键新技能。 研究小组还将通过在当地K-12学校开展教育活动向公众推广。技术摘要由于许多有序材料中的分子堆积结构，半导体聚合物中的电荷传输通常被描述为二维的。 最近的观察表明，在半导体聚合物中形成分级纳米结构，并提出多维传输途径的可能性。 该项目旨在通过计算和实验结果之间的反馈来加速材料的发现。 该研究小组将开发高效的计算方法来预测导致分层3D传输路径的处理方法和材料。 该研究的一个目标是开发新的计算方法，用于大规模并行计算，以利用计算硬件的进步。 新的共轭聚合物将在理论指导下设计，并将进行物理测量以基准计算框架，以了解溶液浇铸过程中结构的演变。 可扩展的模型，以了解在半导体聚合物中的电荷传输畴界的作用，将开发使用电子显微镜的结构图。 开放源代码和大型数据集的基准计算研究的半导体聚合物中的电荷传输是一个重点的研究。

NON-TECHNICAL SUMMARYOrganic polymers are pervasive in modern everyday life, and they have enabled advances in areas ranging from health care to computer technology. The great promise of polymers is their versatility. For example, recent systems have been designed to conduct electricity. Conducting polymers are potential game-changers because they can be dissolved in solvents and printed like inks on flexible substrates for low-cost electronics and sensors. Fabrication of electronic devices using conducting polymers requires the ability to predict how the printing process affects their performance. The PIs and students on this project will develop new materials using an approach in which computational methods guide the design and accelerate the discovery of high performance conducting polymers. The project has significant impact on the future scientific and engineering workforce with graduate student researchers gaining critical new skills in combining computer simulations and physical experiments. The research team will also perform outreach to the public through educational activities in local K-12 schools.TECHNICAL SUMMARYCharge transport in semiconducting polymers is typically described as two-dimensional due to the molecular packing structure in many ordered materials. Recent observations suggest that hierarchical nanostructures form in semiconducting polymers and suggest the possibility of multi-dimensional transport pathways. This project aims to accelerate discovery of materials through feedback between computational and experimental results. The research team will develop highly efficient computational methodologies to predict processing methods and materials that lead to hierarchical 3D transport pathways. A goal of the research is to develop new computational methodologies for massively parallel computations to take advantage of advances in computational hardware. New conjugated polymers will be designed with guidance from theory, and physical measurements will be made to benchmark the computational framework, in order to understand the evolution of structure during solution casting. Scalable models to understand the role of domain boundaries in charge transport in semiconducting polymers will be developed using structural maps from electron microscopy. Open source codes and large datasets for benchmarking computational studies of charge transport in semiconducting polymers are a focus of the research.