DMREF: Paired ionic-electronic conductivity in self-assembling conjugated rod-ionic coil segmented copolymers and mesogens with ionic liquid units

可以预测材料组织和行为的计算机方法，结合性能测量，有可能加速寻找新材料的新兴应用。该项目将确定由大聚合物分子和小尺寸液晶构建的新材料，这些材料在同一分子中结合了联合收割机的两种导电性。这些材料可以形成像“导线”一样的区域，通过一种类型的液体状导线传输带电原子（离子），并通过另一种类型的固体导线传输电子。这些类型的分子允许设计不同尺寸和形状的导电通道。这种材料联合收割机结合了未来机器人以及能量储存和生产所需的各种功能。这项材料发现研究将由一个精通计算科学和工程、聚合物化学、聚合物工程和物理性质研究的跨学科团队进行。本DMREF计划中的劳动力培训将提供一个激励和建设性的环境，参与者将接触到全面的材料设计周期。该项目的重点还提供了一个令人信服的和独特的背景下，传达科学和工程的重要性，以学生，通过与公众和高中和大学的学生既定计划的伙伴关系促进。DMREF学生将集体参与研究和教学，以代表性不足的群体。在该项目下开发的数据、材料设计和机器学习软件将有助于材料基因组倡议的国家基础设施，并可供更广泛的科学和工业界使用。混合离子/电子导体已被证明使用传统的繁琐的研究方法，有希望的能量存储材料，并拥有意想不到的协同作用之间的导电相。该DMREF计划将通过使用双迭代循环来研究自组装聚合物和低聚物液晶混合导体的一般相行为和传输动力学，从而加速发现新的有前途的混合离子/电子导体。 机器学习方法将结合联合收割机和神经网络方案，遗传算法提出候选材料的连续代，神经网络方案构建回归模型，以将输入（化学基团和结构库）与输出变量（导电性）相关联。该项目的智力价值的一个重要方面在于开发过程，以整合计算，实验和数据分析，用于这些功能材料的设计。设想所提出的研究不仅揭示了结构对相行为和电荷传输的作用，以及它们对固体状电子导电相和液体状离子导电区域之间的界面锐度和导电性的影响，而且还导致了新的应用，如能量存储，传感和机器人材料。合作将为该项目提供额外的专业知识。该项目由DMREF倡议和材料研究部聚合物项目的资金支持。该奖项反映了NSF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。

Computer methods that can predict materials organization and behavior, combined with property measurements, have the potential to accelerate the search for new materials for emerging applications. This project will identify new materials built from both large polymer molecules and small-dimension liquid crystals that combine two kinds of conductivity within the same molecule. These materials can form regions that act like "wires" to transport charged atoms (ions) through one type of liquid-like wire, and electrons through another type of solid wire. These types of molecules allow the design of conducting channels of different sizes and shapes. Such materials combine a variety of features needed for future robotics as well as those for energy storage and production. This materials discovery research will be carried out by an interdisciplinary team skilled in computational science and engineering, polymer chemistry, polymer engineering and physical property studies. Workforce training in this DMREF program will provide a stimulating and constructive environment in which participants are exposed to a comprehensive materials design cycle. The focus of the project also provides a compelling and unique context to convey the importance of science and engineering to students, facilitated by partnerships with established programs for the general public and students in high school and university. DMREF students will collectively participate in research and teaching to underrepresented groups. The data, materials design and machine learning software developed under this project will contribute to the Materials Genome Initiative national infrastructure and be accessible by the broader scientific and industrial community. Mixed ionic/electronic conductors have been shown using traditional cumbersome research approaches to have promise for energy storage materials and to possess unexpected synergy between conducting phases. This DMREF program will accelerate the discovery of new promising mixed ionic/electronic conductors by using a dual iterative cycle to study the general phase behavior and transport dynamics of self-assembling polymeric and oligomeric liquid crystal mixed conductors. A machine learning approach will combine a genetic algorithm to propose successive generations of candidate materials, and a neural network scheme to construct a regression model to correlate input (a library of chemical groups and structures) with output variables (conductivity properties). An important aspect of the intellectual merit of this project lies in the development of processes to integrate computation, experiment and data analysis for the design of these functional materials. The proposed research is envisioned to not only shed light on the role of structure on phase behavior and charge transport, and their effect on interface sharpness and conductivity between the solid-like electronically conducting phase and the liquid-like ionically conducting regions but also lead to new applications as energy storage, sensing and robotic materials. Collaborations will provide additional expertise to this program.This project is supported by funds from the DMREF initiative and the Polymers Program in the Division of Materials Research.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.