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绝佳的机会。如果能够加速塑料电子系统的发展，将实现阶梯式变化。例如，可以产生新的可持续技术，将半透明的太阳能电池与温室结合起来，产生一种新型的可持续、零能耗、环境可控的农业;协助汽车和办公楼的智能和可控的热量管理;和/或允许设计新颖的医疗保健设备。尽管过去为进一步推进这些材料和技术平台做出了大量努力，但设计和加工协议仍然基于试错法，受到塑料半导体高度复杂结构（包括复杂结构动力学）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。提出引入新的聚合物物理和多学科ML/AI方法来加速材料开发。技术描述：在这项DMREF研究中，将提供一个框架来扩展聚合物物理概念。该框架将考虑最先进的聚合物半导体具有不溶性的复杂电子给体-受体（D-A）主链以及提供溶解性的大侧链。我们的愿景是推进一个知识平台，实现多组分塑料半导体油墨的可预测和可控自组装，从而实现目标器件特性。这将需要该框架准确地捕获现代D-A半导体聚合物的分子相互作用和复杂的二级和三级结构，并能够开发描述符，这些描述符基于其复杂的初级化学分子设计来指示和分类这些感兴趣的大分子的组装。该项目的目标将通过结合建模自组装，聚合物物理/热相行为，弛豫分析和分子包装/组装的学术研究，与国家实验室合作伙伴在溶液散射（NIST）和理论/建模（LANL）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理。因此，可以预期该项目将对大分子科学和技术产生广泛的影响。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训练有素的高度跨学科的工作队伍，以及一个更了解材料基因组计划好处的普通社区。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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 insolu-ble, 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.