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.
Highlights
The solid electrolyte interphase (SEI), a nanoscale film that forms from electrolyte decomposition at the anodes of lithium-ion batteries (LIBs) during initial charging, is a critical component of modern rechargeable LIB electrolytes[1]
The SEI is formed as a result of numerous competitive reactive processes occurring simultaneously over time scales ranging from picoseconds[4] to days[5]
The remainder of this Data Descriptor is organized as follows: first, we describe the computational methods used to both generate a set of candidate molecules and calculate their properties using density functional theory (DFT) (Fig. 1)
Summary
The solid electrolyte interphase (SEI), a nanoscale film that forms from electrolyte decomposition at the anodes of lithium-ion batteries (LIBs) during initial charging, is a critical component of modern rechargeable LIB electrolytes[1]. LIBE includes non-polymeric and nonoligomeric molecules relevant to SEI formation in LIBs, with molecular properties such as optimized geometries, molecular thermochemistry, and vibrational spectra calculated using DFT. These molecules, which include both species previously reported in the literature as well as many novel species, could form at the SEI as a result of. BonDNet was able to predict heterolytic and homolytic bond dissociation energies with mean absolute error (MAE) far below chemical accuracy (0.022 eV vs chemical accuracy of 0.043 eV) The remainder of this Data Descriptor is organized as follows: first, we describe the computational methods used to both generate a set of candidate molecules and calculate their properties using DFT (Fig. 1). We describe the codes used to generate and analyze LIBE, all of which are freely available in open source repositories
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