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

这一研究项目旨在揭示液体电解液和电池性能之间的复杂关系，这对开发下一代充电电池至关重要。通过系统地改变电解液的组成和浓度，研究小组将确定最佳解决方案。该项目与材料基因组计划无缝结合，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液的基本相互作用。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，研究成果还将有助于STEM教育和公众对储能的认识，为所有人培养更光明、更绿色的未来。电解液中的基本相互作用直接决定了块状电解液的溶剂化结构、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关系，对于探索它们的工作机理，实现电池电解液的合理设计具有重要意义。该建议旨在(1)通过高通量多峰表征方法来更好地了解溶剂化结构；(2)通过高通量计算筛选研究，通过研究传输特性来关联结构-性质关系；(3)将创建基于机器学习的数据分析平台，通过分析高通量结构和模拟数据来预测和识别电池特性。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NSF的法定使命，并通过使用基金会的智力优势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.