Literature DB >> 34354089

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

Evan Walter Clark Spotte-Smith1,2, Samuel M Blau3, Xiaowei Xie2,4, Hetal D Patel1,2, Mingjian Wen1,2, Brandon Wood5, Shyam Dwaraknath2, Kristin Aslaug Persson6,7.   

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.
© 2021. The Author(s).

Entities:  

Year:  2021        PMID: 34354089     DOI: 10.1038/s41597-021-00986-9

Source DB:  PubMed          Journal:  Sci Data        ISSN: 2052-4463            Impact factor:   6.444


  24 in total

1.  Ab initio molecular dynamics simulations of the initial stages of solid-electrolyte interphase formation on lithium ion battery graphitic anodes.

Authors:  Kevin Leung; Joanne L Budzien
Journal:  Phys Chem Chem Phys       Date:  2010-05-25       Impact factor: 3.676

2.  Identifying the components of the solid-electrolyte interphase in Li-ion batteries.

Authors:  Luning Wang; Anjali Menakath; Fudong Han; Yi Wang; Peter Y Zavalij; Karen J Gaskell; Oleg Borodin; Dinu Iuga; Steven P Brown; Chunsheng Wang; Kang Xu; Bryan W Eichhorn
Journal:  Nat Chem       Date:  2019-08-19       Impact factor: 24.427

3.  Standard grids for high-precision integration of modern density functionals: SG-2 and SG-3.

Authors:  Saswata Dasgupta; John M Herbert
Journal:  J Comput Chem       Date:  2017-02-24       Impact factor: 3.376

4.  Theoretical studies to understand surface chemistry on carbon anodes for lithium-ion batteries: reduction mechanisms of ethylene carbonate.

Authors:  Y Wang; S Nakamura; M Ue; P B Balbuena
Journal:  J Am Chem Soc       Date:  2001-11-28       Impact factor: 15.419

5.  ωB97X-V: a 10-parameter, range-separated hybrid, generalized gradient approximation density functional with nonlocal correlation, designed by a survival-of-the-fittest strategy.

Authors:  Narbe Mardirossian; Martin Head-Gordon
Journal:  Phys Chem Chem Phys       Date:  2014-01-16       Impact factor: 3.676

6.  Greater risk of Borrelia burgdorferi infection in dogs than in people.

Authors:  T R Eng; M L Wilson; A Spielman; C C Lastavica
Journal:  J Infect Dis       Date:  1988-12       Impact factor: 5.226

7.  Theoretical studies to understand surface chemistry on carbon anodes for lithium-ion batteries: how does vinylene carbonate play its role as an electrolyte additive?

Authors:  Yixuan Wang; Shinichiro Nakamura; Ken Tasaki; Perla B Balbuena
Journal:  J Am Chem Soc       Date:  2002-04-24       Impact factor: 15.419

8.  Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit.

Authors:  Noel M O'Boyle; Chris Morley; Geoffrey R Hutchison
Journal:  Chem Cent J       Date:  2008-03-09       Impact factor: 4.215

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  2 in total

1.  Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining.

Authors:  Mingjian Wen; Samuel M Blau; Xiaowei Xie; Shyam Dwaraknath; Kristin A Persson
Journal:  Chem Sci       Date:  2022-01-11       Impact factor: 9.825

2.  BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules.

Authors:  Mingjian Wen; Samuel M Blau; Evan Walter Clark Spotte-Smith; Shyam Dwaraknath; Kristin A Persson
Journal:  Chem Sci       Date:  2020-12-08       Impact factor: 9.825

  2 in total

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