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RESEARCH PAPER · Next Energy · 2024

Speeding up the development of solid state electrolyte by machine learning

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Speeding up the development of solid state electrolyte by machine learningOriginalIn situ electrochemical redox tuning of MXene-Co-MOF to MXene/TiO2@Co3O4 nanosheet with enhanced activity and stability15Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook31Accelerating discovery and design of high-performance solid-state electrolytes: a machine learning approach13Recent Applications of Theoretical Calculations and Artificial Intelligence in Solid-State Electrolyte Research: A Review39Atomistic simulation of batteries via machine learning force fields: From bulk to interface16Knowledge-driven eutectic electrolyte design for Zn-ion batteries4Application-oriented design of machine learning paradigms for battery science69Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects18Understanding solid-state battery electrolytes using atomistic modelling and machine learning107Multi-level structural modulation enables fast lithium-ion transport in inorganic solid-state batteries8AI-driven approaches for navigating battery complexity1Artificial intelligence-driven electrolyte chemistry for lithium-ion batteries: mechanisms, progress and strategic perspectives0Machine Learning‐Guided Design of Electrolytes for Rechargeable Batteries4Artificial intelligence for energy materials research: From classical machine learning to large models4Data-driven inorganic material design for future all-solid-state batteries: Innovations and perspectives2Data driven intelligent research and development of gel electrolyte: Methods, challenges and cutting-edge trends1Machine learning toward electrochemical energy storage materials and devices: A review5Machine-learning-accelerated discovery of amorphous lithium-ion electrolytes1High-entropy solid-state electrolytes for next-generation all-solid-state lithium batteries0Artificial Intelligence for Functional Battery Materials: From Multimodal Mechanism Mining to Closed‐Loop Autonomous Optimization0Physics‐Guided Descriptors Enable Data‐Efficient Prediction of Battery Coulombic Efficiency0Automatic Generation of Inorganic Solid‐State Electrolytes via Unified Multi‐Modal Network0

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  1. In situ electrochemical redox tuning of MXene-Co-MOF to MXene/TiO2@Co3O4 nanosheet with enhanced activity and stability

    Next Energy · 2024-09-05

    Xiangfei Sun, Kunfeng Chen, Dongfeng Xue

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  2. Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook

    Advanced Science · 2024-11-18

    Pengcheng Xue, Rui Qiu, Chuchuan Peng, Zehang Peng, Kui Ding, Rui Long, Liang Ma, Qifeng Zheng

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  3. Accelerating discovery and design of high-performance solid-state electrolytes: a machine learning approach

    Physical Chemistry Chemical Physics · 2025-01-01

    Ram Sewak, Vishnu Sudarsanan, Hemant Kumar

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  4. Recent Applications of Theoretical Calculations and Artificial Intelligence in Solid-State Electrolyte Research: A Review

    Nanomaterials · 2025-01-30

    Ming-Wei Wu, Zheng Xi Wei, Yan Zhao, Qiu He

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  5. Atomistic simulation of batteries via machine learning force fields: From bulk to interface

    Journal of Energy Chemistry · 2025-03-18

    Jinkai Zhang, Yaopeng Li, Ming Chen, Jiaping Fu, Liang Zeng, Xi Tan, Tian Shi Sun, Guang Feng

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  6. Knowledge-driven eutectic electrolyte design for Zn-ion batteries

    Chemical Engineering Journal · 2025-03-18

    Jieyu Chang, Qianqian Liu, Chun Sun, Jinhua Hu, Jiabao Bai, Guoxian Li, Chuizhou Meng, Liguang Wang

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  7. Application-oriented design of machine learning paradigms for battery science

    npj Computational Materials · 2025-04-02

    Ying Wang

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  8. Machine Learning in the Design and Performance Prediction of Organic Framework Membranes: Methodologies, Applications, and Industrial Prospects

    Membranes · 2025-06-11

    Tong Wu, Jiawei Zhang, Qinghao Yan, Jingxiang Wang, Hao Yang

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  9. Understanding solid-state battery electrolytes using atomistic modelling and machine learning

    Nature Reviews Materials · 2025-06-24

    Ana C. C. Dutra, Benedek A. Goldmann, M. Saïful Islam, James Alexander Dawson

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  10. Multi-level structural modulation enables fast lithium-ion transport in inorganic solid-state batteries

    Chemical Society Reviews · 2025-11-28

    Tianpeng Huang, Yue Zheng, Deye Sun, Jun Ma, Pengxian Han, Guanglei Cui

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  11. AI-driven approaches for navigating battery complexity

    EES batteries. · 2026-01-01

    Sai Gowtham Allu, Rehan Hasnain, Andrew Vos, Paraskevi Flouda

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  12. Artificial intelligence-driven electrolyte chemistry for lithium-ion batteries: mechanisms, progress and strategic perspectives

    Energy & Environmental Science · 2026-01-01

    Jian Chen, Nan Qin, Fei Pei, Yu Zhang, Yun Qiao, Yifei Yu, Yunhui Huang

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  13. Machine Learning‐Guided Design of Electrolytes for Rechargeable Batteries

    Advanced Materials Technologies · 2026-01-20

    Sathya Narayanan Jagadeesan, Mihir Kalvakaalva, Jessica Liu, Jagjit Nanda, Xueli Zheng

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  14. Artificial intelligence for energy materials research: From classical machine learning to large models

    ENGINEERING Energy · 2026-02-01

    Mingxi Jiang, Jie Zhou, Yanggang An, Zhengran Lin, Menghao Yang

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  15. Data-driven inorganic material design for future all-solid-state batteries: Innovations and perspectives

    Future Batteries · 2026-02-01

    Xiao Chen, Kai Shi, C. Q. Jiang, Yunhai Zhu, Xiaoliang Yu

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  16. Data driven intelligent research and development of gel electrolyte: Methods, challenges and cutting-edge trends

    Green Energy & Environment · 2026-04-01

    Yuanchuan Ren, Renjie Huang, Shiyong Zhao, Xuejun Zhu, Yuhang Lin, Tingfeng Su, Cheng Wang, Nanqi Ren

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  17. Machine learning toward electrochemical energy storage materials and devices: A review

    Sustainable materials and technologies · 2026-04-06

    Chong Ma, Chen Yao, Jing Xu, Yibing Xie

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  18. Machine-learning-accelerated discovery of amorphous lithium-ion electrolytes

    Computational Materials Science · 2026-04-21

    Areg A. Hunanyan, Hayk Zakaryan

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  19. High-entropy solid-state electrolytes for next-generation all-solid-state lithium batteries

    Journal of Alloys and Compounds · 2026-08-17

    Jiana Cao, Yang Li, Jianguang Xu, Jing Li, Yun Zhao, Chuyi Xie

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  20. Artificial Intelligence for Functional Battery Materials: From Multimodal Mechanism Mining to Closed‐Loop Autonomous Optimization

    Advanced Functional Materials · 2026-09-03

    Zhiqiang Wang, Yang Zhou, Wujie Song, Xingjian Yao, Kunfeng Chen, Jun Liu, Dongfeng Xue

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  21. Physics‐Guided Descriptors Enable Data‐Efficient Prediction of Battery Coulombic Efficiency

    Advanced Science · 2026-09-08

    Qintao Sun, Xuewei Gu, Yulin Jie, Hao Yang, Shuhong Jiao, Chao Tang, Yangping Sheng, Yuhao Lu, Ruiguo Cao, Tao Cheng

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  22. Automatic Generation of Inorganic Solid‐State Electrolytes via Unified Multi‐Modal Network

    Small · 2026-10-08

    Yihong Liu, Jinjiang Liang, Zheng Wang, Mohamed Ait Tamerd, Jielun Huang, Renyuan Zhang, Wei Liu, Menghao Yang

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