Quantum chemical calculations of lithium-ion battery electrolyte and interphase species

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 e...

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Bibliographic Details
Published in:Scientific data 2021-08, Vol.8 (1), p.203-203, Article 203
Main Authors: Spotte-Smith, Evan Walter Clark, Blau, Samuel M., Xie, Xiaowei, Patel, Hetal D., Wen, Mingjian, Wood, Brandon, Dwaraknath, Shyam, Persson, Kristin Aslaug
Format: Article
Language:English
Subjects:
639/638/161/891
639/638/563/979
639/638/675
Batteries
Computer applications
Data Descriptor
Datasets
Density functional theory
Electrolytes
Energy
ENERGY STORAGE
Humanities and Social Sciences
Interphase
Learning algorithms
Lithium
Machine learning
multidisciplinary
Salts
Science
Science (multidisciplinary)
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Summary: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 ω B97X-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. Measurement(s) molecule • solid electrolyte interphase Technology Type(s) density functional theory • computational modeling technique Factor Type(s) bond type • charge • spin multiplicity Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14915256
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-021-00986-9