AI-Driven Analysis of Electrolyte Engineering for Batteries
Advanced batteries have emerged as critical components of clean and sustainable energy systems, which have sparked extensive interest and rapid development over the past decade. The electrolyte, referred to as the “blood” of the battery, serves as the essential medium for ion transport, largely determining overall battery performance and has drawn increasing attention. This study conducts an artificial intelligence (AI)- and data-driven analysis of electrolyte engineering based on publications from 2016 to 2025, aiming to trace current research trends and emerging focuses of articles in terms of years, journals, originating countries, institutions, authors, study types, and different subject areas. By employing several refined keyword searches on battery and electrolyte in the Web of Science (WoS) Database, 75,627 articles were extracted for systematic analysis. The analysis reveals a concentrated research landscape, with lithium-based batteries, particularly lithium-ion batteries (LIBs), constituting over 60% of the selected articles. In batteries, electrolyte engineering is heavily shaped by cathode/anode materials and the imperative to solve interfacial challenges of solid electrolyte interphase (SEI)/cathode electrolyte interphase (CEI). Current research on electrolyte engineering exhibits predominantly anode-driven features; electrolytes are specifically tailored to stabilize anode/electrolyte interphase and simultaneously to meet the high-voltage stability requirements imposed by cathodes. Furthermore, a systematic analysis of different electrolyte components and their effects/drawbacks in forming stable interfaces is conducted, with the aims of designing advanced battery systems with high mechanical strength and thermal stability. Noteworthily, this work gives a clear direction, enabling the transition from trial-and-error experimentation to AI-assisted electrolyte design, and further provides “feature-to-target” guidance for the rational design of next-generation electrolytes, aiming to accelerate the development of high-performance and multifunctional battery systems.
Authors
- Xingguo Zhong
- Shuhao Wang (ORCID: https://orcid.org/0000-0003-3206-2073)
- Shiyi Zhang (ORCID: https://orcid.org/0000-0002-8466-3359)
- Yuejun Wang
- Jiawei Wu
- Xizheng Liu
- Huiqiao Li
- Jian Qin
- Yanpeng Guo
- Zihan Li
- Yuan Cheng
Institutions
- Jianghan University (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- MetaResource
- Published
- 2026-11-02
- DOI
- https://doi.org/10.23919/metar.2026.000053
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Science and Technology Program of Hubei Province