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RESEARCH PAPER · npj Computational Materials · 2024

Attention towards chemistry agnostic and explainable battery lifetime prediction

Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel, Leon Merker, Balázs B. Berkes, Leah Nuss, Helge Sören Stein

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Attention towards chemistry agnostic and explainable battery lifetime predictionOriginalAutonomous millimeter scale high throughput battery research system12Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES)0Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES)1Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES)0Improving electrolyte performance for target cathode loading using an interpretable data-driven approach2Trendbericht Festkörperchemie und Materialforschung 20250Data-driven state of health and state of safety estimation for alternative battery chemistries — A comparative review focusing on sodium-ion and LFP lithium-ion batteries17A review and analysis of the safety labeling of lithium-ion batteries22Real-time prediction of battery remaining useful life using hybrid-fusion deep neural networks26Next-generation battery safety management: Machine learning assisted life-time prediction and performance enhancement43Tellurides as Zintl phases?4Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation3Bridging battery degradation and safety: Challenges and opportunities30Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect20Continuous Physics‐Informed Learning Expedited Battery Mechanism Decoupling4History‐Agnostic Lithium‐Ion Batteries State of Health and Remaining Useful Life Estimation Using Fusion‐Based Bayesian Neural Network0Representation learning accelerates the development of models for Li-ion battery health diagnostics and prognostics4A data-driven online framework for rapid identification of aging stages in retired lithium-ion batteries0Interpretable Battery SOH Prediction: A Comparative Interpretability Framework for Multi-Architecture ML Models1Machine learning is rewiring Li-ion battery RUL prediction: A systematic review and outlook0Physics‐Informed Artificial Intelligence for Battery Degradation, Aging, and Lifetime Prediction4Electrochemically constrained transformer with steady-state probability for lithium-ion battery risk evaluation0BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting0Artificial Intelligence for Functional Battery Materials: From Multimodal Mechanism Mining to Closed‐Loop Autonomous Optimization0

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24 direct citing papers saved.

  1. Autonomous millimeter scale high throughput battery research system

    Digital Discovery · 2024-01-01

    Fuzhan Rahmanian, Stefan Fuchs, Bojing Zhang, Maximilian Fichtner, Helge Sören Stein

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  2. Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES)

    ChemRxiv · 2024-01-02

    Fuzhan Rahmanian, Stefan Fuchs, Maximilian Fichtner, Helge Sören Stein

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  3. Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES)

    ChemRxiv · 2024-01-08

    Fuzhan Rahmanian, Stefan Fuchs, Maximilian Fichtner, Helge Sören Stein

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  4. Autonomous millimeter scale high throughput battery research system (Auto-MISCHBARES)

    ChemRxiv · 2024-01-10

    Fuzhan Rahmanian, Stefan Fuchs, Bojing Zhang, Maximilian Fichtner, Helge Sören Stein

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  5. Improving electrolyte performance for target cathode loading using an interpretable data-driven approach

    Cell Reports Physical Science · 2024-12-18

    Vidushi Sharma, Andy Tek, Khanh Nguyen, Maxwell J. Giammona, Murtaza Zohair, Linda K. Sundberg, Young‐Hye La

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  6. Trendbericht Festkörperchemie und Materialforschung 2025

    Nachrichten aus der Chemie · 2025-01-01

    Gregor Kieslich, Simon Steinberg

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  7. Data-driven state of health and state of safety estimation for alternative battery chemistries — A comparative review focusing on sodium-ion and LFP lithium-ion batteries

    Future Batteries · 2025-01-18

    Erik Vanem, Shuai Wang

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  8. A review and analysis of the safety labeling of lithium-ion batteries

    Journal of Energy Storage · 2025-04-02

    Hayder Ali, Hassan Abbas Khan, Muhammad Khalid, MICHAEL G. PECHT

    Read original paper ↗ ·

  9. Real-time prediction of battery remaining useful life using hybrid-fusion deep neural networks

    Energy · 2025-05-17

    Jingyuan Zhao, Xudong Qu, Yuqi Li, Jinrui Nan, Andrew F. Burke

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  10. Next-generation battery safety management: Machine learning assisted life-time prediction and performance enhancement

    Journal of Energy Chemistry · 2025-06-11

    Lei Wang, Yong Qiu, Wenchuang Yuan, Yun Tian, Zhen Zhou

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  11. Tellurides as Zintl phases?

    Journal of Physics Condensed Matter · 2025-06-17

    Simon Steinberg

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  12. Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

    Advanced Science · 2025-10-08

    Benediktus Madika, Chen Kang, JooSung Shim, Taemin Park, Jung Hyeon Moon, EunAe Cho, Seungbum Hong

    Read original paper ↗ ·

  13. Bridging battery degradation and safety: Challenges and opportunities

    eTransportation · 2025-10-11

    Yiwen Zhao, Zhenpo Wang, Lisen Yan, Zhenyu Sun, Peng Jie Liu, Lei Zhang, Weihan Li

    Read original paper ↗ ·

  14. Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect

    Advanced Energy Materials · 2025-10-21

    T. Wang, Kun‐Yu Liu, Hong‐Jie Peng, Xinyan Liu

    Read original paper ↗ ·

  15. Continuous Physics‐Informed Learning Expedited Battery Mechanism Decoupling

    Advanced Science · 2025-10-27

    Shanling Ji, Jun Yuan, Bojing Zhang, Alexey O. Sanin, Leon Merker, Zhisheng Zhang, Jianxiong Zhu, Helge Sören Stein

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  16. History‐Agnostic Lithium‐Ion Batteries State of Health and Remaining Useful Life Estimation Using Fusion‐Based Bayesian Neural Network

    IET Renewable Power Generation · 2026-01-01

    Elahe Sadat Ahmadi Mousavi, Farzaneh Abdollahi, Farshad Barazandeh, Farschad Torabi, Amin Hajizadeh

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  17. Representation learning accelerates the development of models for Li-ion battery health diagnostics and prognostics

    Energy storage materials · 2026-01-12

    Quanquan Zhang, Quanquan Zhang, Mingyu Yang, Guanxi Sun, Yue Xiang, Shitong Wang, Junying Zhang, Shuangqi Li

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  18. A data-driven online framework for rapid identification of aging stages in retired lithium-ion batteries

    Journal of Energy Storage · 2026-04-22

    Yu Han, Cui-ping Zhang, Y B Li, Rui-yi Zhang, H Wang, Peng Li, Yuge Bai, D J Li

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  19. Interpretable Battery SOH Prediction: A Comparative Interpretability Framework for Multi-Architecture ML Models

    Journal of The Electrochemical Society · 2026-05-26

    Shafiyee Islam, Gon Namkoong

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  20. Machine learning is rewiring Li-ion battery RUL prediction: A systematic review and outlook

    Journal of Energy Chemistry · 2026-05-28

    Kaifei Tu, Wei Yuan, Xuyang Wu, Xiaoqing Zhang, Qing Liu

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  21. Physics‐Informed Artificial Intelligence for Battery Degradation, Aging, and Lifetime Prediction

    Battery energy · 2026-07-01

    Mohamad Fani Sulaima, Sunny Arora, Robert Frischer, Hamidreza Namazi

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  22. Electrochemically constrained transformer with steady-state probability for lithium-ion battery risk evaluation

    Journal of Power Sources · 2026-07-17

    Guanhao Li, Yong Gao, Duanfeng Chu, Ao Chi, Qingsong Jiang, Juntian Wang, Wen Fu

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  23. BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting

    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · 2026-08-06

    Ruifeng Tan, Jintao Dong, Weixiang Hong, Jia Li, Jiaqiang Huang, Tong‐Yi Zhang

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  24. 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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