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RESEARCH PAPER · Joule · 2024

Deep learning of experimental electrochemistry for battery cathodes across diverse compositions

Peichen Zhong, Bowen Deng, Tanjin He, Zhengyan Lun, Gerbrand Ceder

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42 direct citing works

Deep learning of experimental electrochemistry for battery cathodes across diverse compositionsOriginalCollaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning156Deep-learning-assisted insights into molecular transport in heterogeneous electrolyte films on electrodes3Metastable layered lithium-rich niobium and tantalum oxides via nearly instantaneous cation exchange2Remaining Useful Life Prediction of Li-Ion Batteries by Multi-modal Attention Fusion Network1Machine learning in advancing anode materials for Lithium-Ion batteries – A review17Mechanical and durability properties of coal cinder concrete: Experimental study and GPR-based analysis7Disentangling multifactorial impacts on cathode thermochemical properties with explainable machine learning9The Synergy Between Modified Electrodes and Artificial Intelligence: Advancing Electrochemical Applications Through Data-Driven Approaches. A Review0Optimising the regeneration process of spent lithium‑cobalt oxide cathode through performance analysis model10Variable and intelligent catalyst design based on local chemical environments in sulfur redox reactions53Shatter existing lifetime limit of lithium-ion battery9Disordered Rocksalts as High‐Energy and Earth‐Abundant Li‐Ion Cathodes43Accelerated development of battery materials leveraging artificial intelligence and automation3State‐of‐the‐Art Machine Learning Technology for Sustainable Lithium Battery Cathode Design: A Perspective13Multitarget Generate Electrolyte Additive for Lithium Metal Batteries8Rare earth elements as emerging catalysts: A review of their role in enhancing DLFC and PEMFC performance5Next-generation lithium-ion batteries for electric vehicles: Advanced materials, AI driven performance optimization, and circular economy strategies23Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery42Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery4Generative Artificial Intelligence Navigated Development of Solvents for Next Generation High‐Performance Magnesium Batteries17Transformative applications of artificial intelligence in lithium battery materials science: advancements and future prospects0Sustainable Direct Recycling of Spent Lithium‐Ion Batteries: Closed‐Loop Regeneration and AI‐Optimized Systems Toward Next‐Generation Battery Circular Economy2Machine-Learning-Accelerated Development of High-Nickel NCM Cathodes via Multivariable Co-optimization3AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model6Revolutionizing Battery Innovation through Artificial Intelligence: The Next Frontier0High-entropy oxides for catalytic applications: From synthesis to active-site modulation and computational insights9Extrapolative prediction of polymer properties using physics-informed hierarchical descriptors4Toward smart CO 2 capture by the synthesis of metal organic frameworks using large language models3AI for Materials Development and Degradation Analysis in Automotive, Battery and Nuclear Industries0Cognitive AI beyond prediction: toward reasoning and discovery1Coupled electrochemical–thermal modelling of early-stage degradation processes in lithium-rich disordered rocksalt cathodes2Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali‐Metal‐Ion Battery Materials4Probing the composition space of V, Fe, and Ti substituted sodium manganese oxides for next-generation sodium-ion cathodes0Review on Recent Development of Artificial Intelligence and Machine Learning Approaches in Energy Applications0Artificial neural networks for sodium-ion batteries: A molecular descriptor approach to performance prediction1MEMFNet: Toward a knowledge-guided paradigm for interpretable electrochemical performance prediction4Data-driven inorganic material design for future all-solid-state batteries: Innovations and perspectives2Coupling Data‐Driven and Reinforcement Learning for Material Development and Device Management in Batteries4Co-designing collectors, electrolytes, and electrodes for sustainable batteries: a roadmap of techno-economics, circularity, and machine learning1Artificial intelligence-driven paradigms for layered cathode materials: A systematic framework for multi-scale engineering2Data-driven development of a universal quantitative metric for lithium-ion battery cathode rate performance0Data-driven design of high-energy-density disordered rock-salt cathode via a physically informed graph neural network1From polyolefins to functionally integrated materials: A review of lithium-ion battery separator innovations0Peukert‐Informed Prediction and Screening of Manganese‐Based High‐Rate Potassium Cathode0Artificial Intelligence for Functional Battery Materials: From Multimodal Mechanism Mining to Closed‐Loop Autonomous Optimization0State of health estimation for lithium-ion batteries using dual-modal hierarchical interactive fusion network based on Gramian angular field of low and medium-frequency electrochemical impedance spectroscopy0Physics-driven deep learning for inverse extraction of capacitive parameters and faradaic contribution in supercapacitors0Unlocking the anionic redox reaction in lithium-rich manganese-based oxides: Mechanisms, challenges, and modification strategies0

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  2. Deep-learning-assisted insights into molecular transport in heterogeneous electrolyte films on electrodes

    Cell Reports Physical Science · 2024-09-01

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  3. Metastable layered lithium-rich niobium and tantalum oxides via nearly instantaneous cation exchange

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  4. Remaining Useful Life Prediction of Li-Ion Batteries by Multi-modal Attention Fusion Network

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  5. Machine learning in advancing anode materials for Lithium-Ion batteries – A review

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  6. Mechanical and durability properties of coal cinder concrete: Experimental study and GPR-based analysis

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  7. Disentangling multifactorial impacts on cathode thermochemical properties with explainable machine learning

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  8. The Synergy Between Modified Electrodes and Artificial Intelligence: Advancing Electrochemical Applications Through Data-Driven Approaches. A Review

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  9. Optimising the regeneration process of spent lithium‑cobalt oxide cathode through performance analysis model

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  10. Variable and intelligent catalyst design based on local chemical environments in sulfur redox reactions

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  11. Shatter existing lifetime limit of lithium-ion battery

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  12. Disordered Rocksalts as High‐Energy and Earth‐Abundant Li‐Ion Cathodes

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  13. Accelerated development of battery materials leveraging artificial intelligence and automation

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  14. State‐of‐the‐Art Machine Learning Technology for Sustainable Lithium Battery Cathode Design: A Perspective

    Advanced Energy Materials · 2025-06-16

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  15. Multitarget Generate Electrolyte Additive for Lithium Metal Batteries

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  16. Rare earth elements as emerging catalysts: A review of their role in enhancing DLFC and PEMFC performance

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  17. Next-generation lithium-ion batteries for electric vehicles: Advanced materials, AI driven performance optimization, and circular economy strategies

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  18. Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery

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  19. Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery

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  20. Generative Artificial Intelligence Navigated Development of Solvents for Next Generation High‐Performance Magnesium Batteries

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  21. Transformative applications of artificial intelligence in lithium battery materials science: advancements and future prospects

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  23. Machine-Learning-Accelerated Development of High-Nickel NCM Cathodes via Multivariable Co-optimization

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  31. Coupled electrochemical–thermal modelling of early-stage degradation processes in lithium-rich disordered rocksalt cathodes

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  32. Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali‐Metal‐Ion Battery Materials

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  33. Probing the composition space of V, Fe, and Ti substituted sodium manganese oxides for next-generation sodium-ion cathodes

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  35. Artificial neural networks for sodium-ion batteries: A molecular descriptor approach to performance prediction

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