Quantum Chemical Calculations of Lithium-Ion Battery Electrolyte and Interphase Species

Abstract

Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte(LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural,thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties.

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

“…Recently, structural and thermodynamic properties have been calculated with DFT for over 17,000 unique electrolyte and interphase species. [276] While empirical correlations exist between thermodynamics and kinetics between competing reactions (Bell-Evans-Polanyi principle [123] ), for practically infinite reaction networks with many almost degenerate branches, like that related to SEI formation, can not be explored solely on thermodynamic numbers. Thus, thermodynamic properties of possible phases are only the tip of the iceberg, information about reaction path determining barriers is missing.…”