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

DMREF：自组装共轭棒离子线圈分段共聚物和具有离子液体单元的介晶中的成对离子电子电导率

• 批准号: 1922259
• 负责人: Fernando Escobedo
• 金额: $ 162.5万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922259&HistoricalAwards=false
• 关键词: DMREF; Paired; ionic; electronic; conductivity; DMREF; Paired; ionic; electronic; conductivity

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.

可以预测材料组织和行为的计算机方法，并结合财产测量，有可能加速寻找新材料的新兴应用程序。该项目将确定由大型聚合物分子和小尺寸液体晶体构建的新材料，它们结合了同一分子内两种电导率。这些材料可以形成像“电线”的区域，以通过一种类型的液态线将带电原子（离子）运输，并通过另一种类型的实心线来运输带电的原子（离子）。这些类型的分子允许设计不同尺寸和形状的通道。这样的材料结合了未来机器人技术所需的各种功能，以及用于存储和生产的功能。该材料发现研究将由熟练的计算科学和工程，聚合物化学，聚合物工程和物理性能研究的跨学科团队进行。该DMREF计划中的劳动力培训将提供一个刺激性和建设性的环境，其中参与者接触到综合材料设计周期。该项目的重点还提供了一个引人入胜且独特的背景，以传达科学和工程学的重要性，这是由与公众和高中和大学的公众建立计划的合作伙伴关系促进的。 DMREF的学生将集体参加代表性不足的小组的研究和教学。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计划国家基础设施，并可以被更广泛的科学和工业社区访问。已经使用传统的繁琐研究方法显示了混合离子/电子导体，以便有望储存材料，并在传导阶段之间具有意想不到的协同作用。该DMREF程序将通过使用双重迭代循环研究新的有希望的混合离子/电子导体的发现，以研究自组装聚合物和低聚物液体晶体混合导体的一般相行为和转运动力学。 一种机器学习方法将结合遗传算法，以提出连续的候选材料，以及一种神经网络方案，以构建回归模型，以将输入（化学基团和结构库）与输出变量（电导率属性）相关联。该项目的智力优点的一个重要方面是开发过程，以整合这些功能材料设计的计算，实验和数据分析。拟议的研究不仅阐明了结构对相行为和电荷传输的作用，以及它们对固体样式电导阶段与液体样离子导电区域之间的界面清晰度和电导率的影响，还导致了作为储能，储能，传感和机器人材料的新应用。合作将为该计划提供更多的专业知识。该项目得到了DMREF计划的资金和材料研究部中的聚合物计划的资金。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准通过评估来支持的。