Evidence for multi-Li+ clustering in lithium-ion battery electrolytes

, despite being parameterized to only quantum-chemical gas-phase cluster calculations, and hence provide a compelling demonstration that machine-learning force-field-based models developed from first principles can robustly represent a complex chemical system to atomic resolution.

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Publication Details

Journal
Nature
Published
2026-10-14
DOI
https://doi.org/10.1038/s41586-026-11138-7
Primary Topic
Advancements in Battery Materials
Type
article
Field-Weighted Citation Impact
0.00

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article

Evidence for multi-Li+ clustering in lithium-ion battery electrolytes

Mathew David Halls, Zachary K. Goldsmith, Richard A. Friesner, Lauren E. Marbella et al.
Nature
Advancements in Battery Materials
article

Evidence for multi-Li+ clustering in lithium-ion battery electrolytes

Mathew David Halls, Zachary K. Goldsmith, Richard A. Friesner, Lauren E. Marbella, John L. Weber, Eric G. Keeler, Rishabh D. Guha, Leif D. Jacobson, Garvit Agarwal, Asya Svirinovsky-Arbeli, Sibali Debnath, Daniel A. Steingart, James Stevenson, M. Mondal, Amy S. Metlay, Lakshmi Bhai, Alexander Urban, Yongbeom Kwon, In Won Yeu
article en

Abstract

, despite being parameterized to only quantum-chemical gas-phase cluster calculations, and hence provide a compelling demonstration that machine-learning force-field-based models developed from first principles can robustly represent a complex chemical system to atomic resolution.

NatureVol. 658(8136)
Schrodinger (United States) (US), New York Structural Biology Center (US), Schrodinger (United States) (US), Columbia University (US)
National Science Foundation, U.S. Department of Energy, National Institutes of Health, Office of Science, Division of Materials Research, Basic Energy Sciences
Openalex Percentile: Top 23%
Advancements in Battery Materials
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