Collaborative Research: DMREF: High-Throughput Screening of Electrolytes for the Next Generation of Rechargeable Batteries

可充电电池已成为电动汽车、电子产品和电网储能最受欢迎的储能设备之一。为下一代可充电电池开发新型电解质需要更多地了解传输特性，微观结构以及微观结构对传输特性的影响。在这个项目中，研究人员将系统地改变电解质的成分和浓度，以确定先进的可充电电池的最佳解决方案。拟议研究的成功将提供高通量实验/表征和机器学习平台。此外，综合研究和教育计划将广泛影响大学，中学教育和公众。研究成果将纳入研究人员的课程，并用于培养跨学科研究领域的本科生和研究生。新的教育推广计划包括每年秋季为当地高中学生和教师举办电解质储能研讨会，以增强该NSF项目的更广泛影响。电解质中的基本相互作用直接决定了本体电解质的溶剂化结构，动力学和电池性能。了解这些复杂的相互作用及其与电解液性能的相关性，对于探索其工作机理，实现电池电解液的合理设计具有重要意义。该提案的新奇在于使用先进的高通量表征，并借助MD模拟和机器学习来确定分子相互作用与电池电解质宏观性质之间的联系。该提案旨在（1）通过多峰表征方法拉曼和X射线获得对溶剂化结构的良好理解，以进行高通量实验/表征。高通量X射线散射技术（APS的USAXS/SAXS/WAXS）将用于表征溶液组织作为离子组成，离子浓度和温度的函数;（2）通过高通量计算筛选研究研究传输特性来关联结构-性能关系。AIMD和MD将开发一个计算平台来筛选结构/性能关系;（3）将创建基于机器学习的数据分析平台，通过分析高通量的结构和模拟数据来预测和识别电池性能，本项目由材料研究部和化学，生物，环境工程和运输系统。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。

Rechargeable batteries have become one of the most popular energy storage devices for electric vehicles, electronics, and grid energy storage. Developing novel electrolytes for the next generation of rechargeable batteries require more understanding of transport properties, microstructures, and the impact of microstructure on transport property. In this project, the investigators will systematically vary the composition and concentration of the electrolytes to determine the optimum solution for advanced rechargeable batteries. The success of the proposed research will provide high throughput experimentation/characterization and machine learning platforms. Moreover, the integrated research and educational programs will broadly impact the university, secondary education, and the general public. The research results will be into the investigators' courses and be used to train undergraduate and graduate students in the interdisciplinary research areas. New educational outreach initiatives include having an Electrolyte for Energy Storage workshop for local high school students and teachers each fall to enhance the broader impact of this NSF project.The fundamental interactions in the electrolyte directly determine the solvation structures, kinetics, and battery performance of the bulk electrolytes. Understanding the complex interactions and their correlation with electrolyte performance is significant for exploring their working mechanisms and realizing the rational design of battery electrolytes. The novelty of this proposal lies in the use of advanced high-throughput characterization with the help of MD simulation and machine learning to determine the link between molecular interactions and the macroscopic properties of battery electrolytes. The proposal aims to (1) gain a good understanding of the solvation structure through multimodal characterization methods Raman and X-ray for high throughput experimentation/characterization. High-throughput X-ray scattering techniques (USAXS/SAXS/WAXS for APS) will be used to characterize solution organization as a function of ion composition, ion concentration, and temperature; (2) to correlate the structure-property relationship by studying transport properties through high-throughput computational screening studies. A computational platform will be developed to screen structure/property relationships by AIMD and MD; (3) A machine learning-based data analysis platform will be created to predict and identify battery properties by analyzing high-throughput structural and simulation data.This project is supported by the Division of Materials Research and the Chemical, Biological, Environmental Engineering and Transport Systems.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.