ArticleScientific data2021
Quantum chemical calculations of lithium-ion battery electrolyte and interphase species.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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Who cites it
17 citing papers in PubMed.
- Evidence for multi-LiNature · 2026Article
- Redefining LiF as a Nanostructured Building Block for Interphase Engineering in Lithium Metal Batteries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states.Digital discovery · 2026Article
- Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks.Journal of the American Chemical Society · 2026Article
- Generative AI-Driven Discovery of Next-Generation Electrolytes for Alkali Metal Batteries.Journal of chemical information and modeling · 2026Article
- Multi-level QTAIM-enriched graph neural networks for resolving properties of transition metal complexes.Digital discovery · 2025Article
- HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions.Journal of chemical information and modeling · 2025Article
- Graph-Theory Algorithm for Prediction of Electrolyte Degradation Reactions in Lithium- and Sodium-Ion Batteries.Materials (Basel, Switzerland) · 2025Article
- CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation.Journal of chemical information and modeling · 2025Article
- Solubilities of Ethylene and Carbon Dioxide Gases in Lithium-Ion Battery Electrolyte.Journal of chemical and engineering data · 2024Article
- A Critical Analysis of Chemical and Electrochemical Oxidation Mechanisms in Li-Ion Batteries.The journal of physical chemistry letters · 2024Article
- Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set.Journal of chemical information and modeling · 2023Article
- A Perspective on Sustainable Computational Chemistry Software Development and Integration.Journal of chemical theory and computation · 2023Article
- Cyclodextrins for Lithium Batteries Applications.Materials (Basel, Switzerland) · 2023Review
- Chemical Reaction Networks Explain Gas Evolution Mechanisms in Mg-Ion Batteries.Journal of the American Chemical Society · 2023Article
- Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining.Chemical science · 2022Article
- BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules.Chemical science · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.