A Data-Driven Approach to Understanding and Predicting the Early Formation of the Solid-Liquid Electrolyte Interphase

A remaining fundamental challenge in the pursuit of rational design of novel and optimized electrolytes is understanding and ultimately predicting the out-of-equilibrium electrochemical reaction cascade responsible for the creation of a functional solid-electrolyte interfaces (SEI). We here advocate that a data-driven approach coupled with a high-throughput quantum chemistry computational infrastructure can successfully address the complexity of such reaction cascades. We have constructed a chemical reaction network which allows for millions of elementary reactions involving bond breaking/formation as well as oxidation/reduction reactions. The reaction energetics are obtained from a computational framework which automate geometry optimization and vibrational frequency calculations for molecules, including radical, charged, metal-coordinated, and solvated species. To date, we have applied this framework to over tens of thousands of unique molecules relevant to the formation of solid-electrolyte interphases in Li-ion and Mg-ion batteries. To identify possible products and analyze reaction pathways, we apply Monte Carlo approaches as well as graph representation. 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 recovers the most favorable reaction paths to form key SEI components, which were carefully identified over the past two decades. Thus, our data-driven approach and infrastructure show promise to accelerate the understanding of chemical reactivity in complex environments, in the aid of novel electrolyte design.