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

Northern Illinois University, Dekalb IL

Investigators

Abstract

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.