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

该研究项目旨在揭示液体电解质与电池性能之间的复杂关系，这对开发下一代可充电电池至关重要。通过系统地改变电解质的组成和浓度，该团队将确定最佳解决方案。该项目与材料基因组计划（Materials Genome Initiative）无缝对接，使用高通量高级表征、计算模拟和机器学习数据驱动分析来了解电解质的基本相互作用。该项目着眼于开发高性能、安全和可持续的能源存储解决方案的长期目标，具有巨大的潜力。除了技术进步之外，研究成果还将有助于STEM教育和公众对储能的认识，为所有人创造一个更光明、更绿色的未来。电解质中的基本相互作用直接决定了散装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质性能的关系，对于探索其工作机制和实现电池电解质的合理设计具有重要意义。该提案旨在(1)通过高通量多模态表征方法获得对溶剂化结构的良好理解；(2)通过高通量计算筛选研究输运性质，建立结构-性质关系；(3)建立基于机器学习的数据分析平台，通过分析高通量结构和仿真数据来预测和识别电池性能。该项目由材料研究部和化学、生物、环境工程和运输系统部支持。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。

This research project aims to uncover the intricate relationships between liquid electrolytes and battery performance, which is crucial for developing next-generation rechargeable batteries. By systematically varying the composition and concentration of electrolytes, the team will identify optimal solutions. This project aligns seamlessly with the Materials Genome Initiative, using high-throughput advanced characterization, computational simulations, and machine learning data-driven analysis to understand fundamental interactions of electrolytes. With a focus on the long-term goal of developing high-performance, safe, and sustainable energy storage solutions, this project holds tremendous potential. Beyond technological advancements, the research outcomes will contribute to STEM education and public awareness about energy storage, fostering a brighter and greener future for all.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 proposal aims to (1) gain a good understanding of the solvation structure through high-throughput multimodal characterization methods; (2) correlate the structure-property relationship by studying transport properties through high-throughput computational screening studies; (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 Division.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.