(Invited) Approaches to Understanding and Predicting the Stability of Liquid Electrolytes and the Early Formation of the Solid-Liquid Electrolyte Interphase

摘要

Electrolytes with improved stability have the potential to transform the current state-of-the-art Li-ion architectures and enable future technologies such as multivalent systems (e.g., Mg~(2+), Ca~(2+) and Zn~(2+)), as well as Li-S and Li-O_2 conversion systems. A remaining challenge in the pursuit of rational design of novel and optimized electrolytes is understanding and ultimately predicting the reaction cascade responsible for the creation of a functional liquid-anode solid-electrolyte interface (SEI). We here present an overview of the challenge, coupled with several examples of systems where minority as well as majority speciation of the electrolyte is imperative to understand the origin of the electrochemical window. Furthermore, to address the complexity of the resulting reaction cascade once these species start to decompose, we advocate the need for data-driven approach coupled with a high-throughput quantum chemistry computational infrastructure. To this end, we have constructed a chemical reaction network with tens of thousands of elementary reactions which allow for bond breaking/formation as well as oxidation/reduction reactions. The reaction energetics are obtained from our computational framework which automate geometry optimization and vibrational frequency calculations for molecules, including radical, charged, metal-coordinated, and solvated species. To analyze possible reaction pathways, we apply a graph representation to our network, leveraging an iterative reweighting strategy that solves for all prerequisite branching such that traditional pathfinding algorithms correctly capture coordinated pathways. Machine-learning algorithms operating on bond-formation and breaking energetics aid in rapid evaluation of highly reactive processes. We show that without any apriori chemical intuition, our automated framework can recover favorable reaction paths to form key SEI components, which have been carefully identified over the past two decades. This data-driven approach and infrastructure show promise to accelerate the understanding of chemical reactivity in complex environments, in the aid of novel electrolyte design.