Evidence for significant multi-Li+ clustering in common lithium-ion battery electrolytes

Lithium-ion battery (LIB) electrolytes have been empirically optimized to maximize battery lifetime and charge rate, but a detailed understanding of how their atomic-scale structure controls their electrochemical behaviors remains lacking. Here, we combine machine-learning-enabled atomistic simulations, nuclear magnetic resonance measurements, electrochemical data, and quantum-chemical calculations to reveal previously unstudied multi-Li+ complexes without anions that are uniquely facile for reduction. This surprising insight explains the success of empirically-chosen solvent mixtures and provides a foundation for the rational design of improved electrolytes. Furthermore, our machine learning force field (MLFF) based LIB electrolyte simulations accurately reproduce a wide range of condensed phase experiments, despite being parameterized only to quantum chemical gas phase cluster calculations, and hence provide a compelling demonstration that MLFF models developed from first principles can robustly represent a complex chemical system to atomic resolution.