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

合作研究：DMREF：建立分子相互作用框架来设计和预测现代聚合物半导体组装

• 批准号: 2324191
• 负责人: Brendan O'Connor
• 金额: $ 100万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324191&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Establishing; molecular

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.

非技术描述：基于塑料的电子材料具有独特的光电和加工特性，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、未来计算选项等发展提供了绝佳的机会。塑料电子系统的发展可以加速，例如，可以产生新的可持续技术，例如带有温室的半透明太阳能电池，以产生新型可持续、零能源、受控环境的农业。 ;协助汽车和办公楼的智能和可控热管理；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验。和错误方法，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，其关键瓶颈是无法用经典术语和经典方法来理解和分类结构-功能关系。建议将人工智能方法引入技术描述：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、复杂的电子供体-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，以实现多组分塑料半导体墨水的可预测和受控自组装，从而获得目标器件特性。该项目的目标将通过结合以下方面的学术研究来实现：现代 D-A 半导体聚合物的分子相互作用以及复杂的二级和三级结构，并能够开发描述符，这些描述符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组装。模拟自组装、聚合物物理/热相行为、松弛分析和分子堆积/组装，国家实验室合作伙伴在溶液散射 (NIST) 和理论/建模 (LANL) 以及靶向合成方面做出了贡献ML/AI 分析和方法将提高实验效率和知识推理，因此，该项目预计还将在高分子科学和技术领域产生广泛的影响。实验方法、理论、模拟和机器学习技术以及广大社区更加了解材料基因组计划的好处。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。