Collaborative Research: DMREF: Establishing a molecular interaction framework to design and predict modern polymer semiconductor assembly

非技术描述：基于塑料的电子材料具有独特的光电和加工性能，为大面积照明应用、新型多功能太阳能收集平台、下一代传感器、未来计算选项等提供了特殊的机会。如果能加快塑料电子系统的发展，就能实现阶梯式的变化。例如，可能产生新的可持续技术，例如，将半透明太阳能电池与温室结合起来，产生一种新的可持续、零能源、可控环境农业；协助汽车和办公楼的智能可控热管理；和/或允许设计新颖的医疗保健设备。尽管过去在进一步推进这些材料和技术平台方面做了大量的努力，但设计和处理协议仍然基于试错方法，受到塑料半导体高度复杂的结构（包括复杂的结构动力学）的阻碍。关键的瓶颈是结构-功能关系不能用经典的命名法和经典的方法来理解和分类。新的聚合物物理和多学科ML/AI方法被提出来加速材料的开发。技术描述：在这个DMREF研究中，将提供一个框架来扩展聚合物物理概念。该框架将考虑到最先进的聚合物半导体具有不溶性，复杂的电子供体-受体（D-A）主链以及提供溶解性的大侧链。愿景是推进一个知识平台，用于可预测和控制的多组分塑料半导体油墨的自组装，从而导致目标器件性能。这将需要框架准确地捕捉分子相互作用和现代D-A半导体聚合物复杂的二级和三级结构，并使描述符的发展能够根据这些有趣的大分子复杂的初级化学分子设计来指示和分类它们的组装。该项目的目标将通过结合自组装建模、聚合物物理/热相行为、松弛分析和分子包装/组装方面的学术研究，以及国家实验室在溶液散射（NIST）和理论/建模（LANL）方面的合作伙伴的贡献，以及众多合成合作者的目标合成来实现。ML/AI分析和方法将提高实验效率和知识推断。因此，该项目有望在大分子科学和技术领域产生广泛的影响。该项目还将提供一支在实验方法、理论、模拟和机器学习技术方面受过高度跨学科培训的劳动力队伍，并使一般社区更加了解材料基因组计划的好处。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。

Non-technical Description: Electronic materials based on plastics possess unique optoelectronic and processing properties that provide exceptional opportunities for large-area lighting applications, novel versatile solar-energy harvesting platforms, next-generation sensors, future computing options, and beyond. If the development of plastic electronics systems can be accelerated, step changes will be achieved. For instance, new sustainable technologies may be produced that, e.g., integrate semi-transparent solar cells with greenhouses to yield a new class of sustainable, zero-energy, controlled-environment agriculture; assist with smart and controllable heat management for cars and office buildings; and/or allow the design of novel health-care devices. Despite extensive past efforts to further advance these materials and technology platforms, design- and processing- protocols remain based on trial-and-error methods, hampered by the highly intricate structure of plastic semiconductors, including complex structural dynamics. The critical bottleneck is that structure-function relations cannot be understood and categorized with classic nomenclature and classic approaches. New polymer physics and multi-disciplinary ML/AI approaches are proposed to be introduced to accelerate materials development. Technical Description: In this DMREF research, a framework will be delivered to expand polymer physics concepts. The framework will consider that state-of-the-art polymer semiconductors have an insoluble, complex electron donor-acceptor (D-A) backbone as well as large sidechains that provide solubility. The vision is to advance a knowledge platform toward the predictable and controlled self-assembly of multicomponent plastic semiconducting inks that lead to targeted device properties. This will require the framework to accurately capture the molecular interactions and the complex secondary and tertiary structures of modern D-A semiconducting polymers and enable the development of descriptors that dictate and classify the assembly of these interesting macromolecules based on their intricate primary chemical molecular design. The project’s objective will be pursued by combining academic research in modeling self-assembly, polymer physics/thermal phase behavior, relaxation analysis, and molecular packing/assembly, with contributions by national laboratory partners in solution scattering (NIST) and theory/modeling (LANL), and targeted synthesis by numerous synthetic collaborators. ML/AI analytics and methodologies will enhance experimental efficiency and knowledge inferences. This project, thus, can be expected to have broad implications in macromolecular science and technology in general. The project will also provide a highly interdisciplinary workforce trained in experimental methodologies, theory, simulations, and machine-learning techniques and a general community more aware of the benefits of the Materials Genome Initiative.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.