CAREER: Chemical Network Based Understanding and Prediction of Electrolyte Decomposition in Batteries

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New types of batteries are needed to sustainably meet growing power demands and the requirements of novel applications like electric vehicles, renewable energy storage, and load leveling. A central challenge in the development of new electrolytes for batteries is poor understanding of the electrolyte degradation pathways that lead to battery failure. This CAREER project will develop computational methods to comprehensively elucidate these electrolyte reactions. Success in this project will transform how electrolytes are designed and implemented using a computational approach before costly synthesis and testing. The resulting new knowledge about electrolyte degradation chemistry and new simulation methodologies will benefit the next generation of scientists and engineers working on batteries. This project’s integrated education plan includes developing hands-on research projects with students and faculty at non-research institutions and developing continuing education components for chemical engineers.The overarching goal of this CAREER project is to establish electrolyte degradation reactions from first principles as the basis for characterizing, optimizing, and designing novel battery electrolytes. The proposed methods will leverage a novel combination of two recent breakthroughs, making this an ideal time to investigate electrolyte degradation problem from a computational perspective. First, we will use modern semi-empirical quantum chemistry to provide the simulation throughout required to comprehensively describe the complex reaction networks associated with electrolyte degradation. Second, we will apply transfer learning models to improve the accuracy of describing reactions in condensed phases and at electrode interfaces. In combination, these strategies will address the tradeoff between computational accuracy and cost that has limited the application of reaction network characterizations to liquid electrolytes. These simulation methodologies will initially be applied to liquid electrolyte formulations that have widespread usage in contemporary Li-ion and post Li-ion batteries. Despite the fact that these are established electrolytes, the degradation chemistry that drives solid-electrolyte interphase formation and electrolyte-related failure is still incompletely resolved. Thus, by focusing on these electrolyte chemistries, we will be able to validate our characterization methodology while also providing the first complete view of the degradation reactions that occur in these electrolytes. The reaction data generated by these characterizations will also be used to create reaction databases that will facilitate machine learning activities aimed at prioritizing or even circumventing more costly physics-based simulations of electrolytes.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.