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，有可能加速寻找用于新兴应用的新材料。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两种导电性。这些材料可以形成像“线”一样的区域，通过一种液体状线传输带电原子（离子），通过另一种固体线传输电子。这些类型的分子允许设计不同大小和形状的导电通道。这种材料结合了未来机器人所需的各种特性，以及能量储存和生产的特性。这项材料发现研究将由一个精通计算科学与工程、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。这个DMREF项目的劳动力培训将提供一个激励和建设性的环境，让参与者接触到一个全面的材料设计周期。该项目的重点还提供了一个引人注目和独特的背景，通过与针对普通公众和高中和大学学生的既定项目的合作，向学生传达科学和工程的重要性。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为材料基因组计划的国家基础设施做出贡献，并由更广泛的科学界和工业界访问。使用传统繁琐的研究方法，混合离子/电子导体已被证明有希望成为储能材料，并在导电相之间具有意想不到的协同作用。该DMREF项目将通过双迭代循环研究自组装聚合物和低聚液晶混合导体的一般相行为和输运动力学，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遗传算法来提出连续几代的候选材料，以及神经网络方案来构建回归模型，将输入（化学基团和结构库）与输出变量（电导率特性）关联起来。本项目智力优势的一个重要方面在于开发了将计算、实验和数据分析相结合的过程来设计这些功能材料。该研究不仅揭示了结构对相行为和电荷输运的作用，以及它们对类固体电子导电相和类液体离子导电区域之间的界面锐度和电导率的影响，而且还导致了能量存储，传感和机器人材料等新应用。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。

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.