ArticleAngewandte Chemie (International ed. in English)2026
A Quantitative Electrostatic Potential Descriptor Enables Deep Learning-Accelerated Discovery of High-Performance Lithium-Ion Battery Electrolytes.
Kun Han, Yu Lou, Junfeng Li, Chaocang Weng, Chenglong Wang, Wenjie Mai, Jinliang Li, Guang Yang, Likun Pan
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In one paragraphArticle in Angewandte Chemie (International ed. in English), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 · Who and what moneyAuthors and funding
9 authors.
Kun HanShanghai Key Laboratory of Magnetic Resonance, School of Physics, Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai, China.
Yu LouShanghai Key Laboratory of Magnetic Resonance, School of Physics, Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai, China.
Junfeng LiCollege of Logistics Engineering, Shanghai Maritime University, Shanghai, China.
Chaocang WengDepartment of Materials Science and Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore.
Chenglong WangShanghai Key Laboratory of Magnetic Resonance, School of Physics, Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai, China.ORCID https://orcid.org/0000-0003-3896-5994 Wenjie MaiSiyuan Laboratory, Guangdong Provincial Engineering Technology Research Center of Vacuum Coating Technologies and New Energy Materials, Department of Physics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou, China.
Jinliang LiSiyuan Laboratory, Guangdong Provincial Engineering Technology Research Center of Vacuum Coating Technologies and New Energy Materials, Department of Physics, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou, China.ORCID https://orcid.org/0000-0002-2200-5123 Guang YangShanghai Key Laboratory of Magnetic Resonance, School of Physics, Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai, China.
Likun PanShanghai Key Laboratory of Magnetic Resonance, School of Physics, Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai, China.ORCID https://orcid.org/0000-0001-9294-1972 Funding
Grand NeoBay Development Fund 202509-KW-1.1.1-001Guangxi Science and Technology Program 2024AB08156
6 · The paper itselfAbstract
Rational electrolyte design for high-energy-density lithium-ion batteries (LIBs) urgently demands precise and quantitative molecular descriptors of solvation power to enable deep learning (DL)-accelerated screening, yet such descriptors remain lacking. Here, we introduce the electrostatic potential ratio |ESP
Indexed as
electrolyte designelectrostatic potentiallithium‐ion batteriesmachine learningsolvation structure
Identifiers
PMID42424155
PMCPMC13549032
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