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RESEARCH PAPER · Materials Horizons · 2025

Machine learning pipelines for the design of solid-state electrolytes

Vinamr Jain, Zhilong Wang, Fengqi You

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Machine learning pipelines for the design of solid-state electrolytesOriginalGeometric deep learning predicts electrostatic potential for high-throughput electrolyte design0Towards an artificial intelligence ecosystem for performance-driven polymer design1Corrosion-inspired design of artificial interphases for lithium-metal solid-state batteries: Mechanisms, protective strategies, and characterization insights1Machine learning drives a new paradigm in inorganic solid-state electrolytes research2Ion-conductive 2D materials beyond graphene for electrochemical energy storage and conversion systems0Stabilizing Electrode/Electrolyte interphases for Lithium-Ion batteries at High-Temperature through integrated electrolyte engineering and surface modification1AI‐Assisted Design of 2D‐Material Electrode for Next‐Generation Wearable Aqueous Zinc Batteries1NASICON-type LATP solid electrolytes for lithium metal batteries: Fundamentals to AI-driven materials design4Frontiers of defect, transport, and reaction theories in complex and driven solid-state ionic materials2Interfacial problems and optimization strategies at the oxide solid-state electrolyte-electrode interface2Optimize Before You Synthesize—Enhancing the Ionic Conductivity of Li 7 SiPS 8 Using Bayesian Optimization0Optimize Before You Synthesize—Enhancing the Ionic Conductivity of Li 7 SiPS 8 Using Bayesian Optimization0Disorder and entropy engineering in solid-state electrolytes for fast Li⁺ and Na⁺ transport2Leveraging Machine Learning to Discover New Solid-State Materials: Topological Insulators, Semiconductors, And Solid Electrolytes Applications (Review Article)0Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries0Interfacial charge screening and ion transport in defect-engineered transition metal oxides based anodes for high-rate multivalent ion storage0Towards high-performance halide electrolytes: Data-driven approaches for materials design and synthesis2High-entropy materials for electrochemical energy storage: Microstructure, phase evolution and performance in batteries and supercapacitors0Machine learning-guided electrolyte–interface co-design for Li-based anode-free batteries: Toward a predictive framework for stable metal deposition3Overcoming the Interface Bottleneck in Solid‐State Batteries: Electrolyte Design, Interface Engineering, and Computational Discovery0Bridging chemistry and composition-space exploration: Particle swarm optimization of multicomponent glassy sulfide electrolytes0Machine Learning and Theoretical Computation Synergy Advancing Halide Electrolytes Toward All‐Solid‐State Lithium Batteries: Recent Advances, Challenges, and Perspectives1Progress on Halide Solid State Electrolytes: Structural Regulation, Interfacial Engineering, and Machine Learning Driven Paradigms2Machine learning for accelerated screening of sodium anode/chalcogenide electrolyte interfaces for all-solid-state sodium batteries0Multiobjective Artificial Intelligence-Assisted High-Throughput Screening and Prioritization of Gel Polymer Electrolytes for Safer Lithium Batteries0Rational doping of oxide solid-state electrolytes for all-solid-state lithium metal batteries: Mechanisms and computational screening strategies0Unveiling the hidden effects of local potential energy surface on ion transport in superionic conductors0A Data‐to‐Interface Framework for AI‐Guided Sodium‐Ion Battery Materials Discovery: From Descriptors to Full‐Cell Decisions0Machine Learning‐Driven Design of Electrolytes and Interfaces for Solid‐State Lithium Batteries: Progress and Outlook0Review of AI-driven discovery of cathode anode and electrolyte materials for advancing sodium-ion batteries0

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  1. Geometric deep learning predicts electrostatic potential for high-throughput electrolyte design

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  2. Towards an artificial intelligence ecosystem for performance-driven polymer design

    Polymer Chemistry · 2026-01-01

    Jie Zhu, Dong Wu, Zhao‐Yan Sun, H. C. Li, Xiayun Huang, Zhihong Nie

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  3. Corrosion-inspired design of artificial interphases for lithium-metal solid-state batteries: Mechanisms, protective strategies, and characterization insights

    Journal of Electroanalytical Chemistry · 2026-02-16

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  4. Machine learning drives a new paradigm in inorganic solid-state electrolytes research

    Computational Materials Today · 2026-02-26

    Xiang Yin, Jun Ma, Shu Zhang, Guanglei Cui

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  5. Ion-conductive 2D materials beyond graphene for electrochemical energy storage and conversion systems

    Energy storage materials · 2026-02-28

    Pritish Kumar Behura, Kane Ho, Jasneet Kaur, Hadis Zarrin

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  6. Stabilizing Electrode/Electrolyte interphases for Lithium-Ion batteries at High-Temperature through integrated electrolyte engineering and surface modification

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    Leqiong Xie, Junchen Zhou, Da Wang, Yunyong Li, Xiangming He, Yingqiang Wu, Yida Deng

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  7. AI‐Assisted Design of 2D‐Material Electrode for Next‐Generation Wearable Aqueous Zinc Batteries

    Small Structures · 2026-04-01

    Shibo Meng, Jinxiu Feng, Jia Ying, Dong Zheng, Wenxian Liu, Fangfang Wu, Wenhui Shi, Xiehong Cao

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  8. NASICON-type LATP solid electrolytes for lithium metal batteries: Fundamentals to AI-driven materials design

    Energy storage materials · 2026-04-18

    Ateeq Ahmed, 노은빈, Jinseo Kim, 유준환, C W Lee, Jina Lee, Jimin Oh, Jimin Oh

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  9. Frontiers of defect, transport, and reaction theories in complex and driven solid-state ionic materials

    MRS Bulletin · 2026-05-01

    Vanessa Woo, Channyung Lee, Elif Ertekin, Nicola H. Perry

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  10. Interfacial problems and optimization strategies at the oxide solid-state electrolyte-electrode interface

    Ionics · 2026-05-11

    Liubin Song, Xinyang Qin, Daoxin Wu, Xingli Xiao, Zhongliang Xiao, Yinjie Kuang, Tingting Zhao, Xue Wen

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  11. Optimize Before You Synthesize—Enhancing the Ionic Conductivity of Li 7 SiPS 8 Using Bayesian Optimization

    Angewandte Chemie International Edition · 2026-05-23

    Lucas G. Balzat, Robert Calaminus, Yinghan Zhao, Kristina Gjorgjevikj, Igor L. Moudrakovski, Simon Krause, Arnd Koeppe, Britta Nestler, Bettina Valeska Lotsch

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  12. Optimize Before You Synthesize—Enhancing the Ionic Conductivity of Li 7 SiPS 8 Using Bayesian Optimization

    Angewandte Chemie · 2026-05-23

    Lucas G. Balzat, Robert Calaminus, Yinghan Zhao, Kristina Gjorgjevikj, Igor L. Moudrakovski, Simon Krause, Arnd Koeppe, Britta Nestler, Bettina Valeska Lotsch

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  13. Disorder and entropy engineering in solid-state electrolytes for fast Li⁺ and Na⁺ transport

    Energy storage materials · 2026-05-27

    Tarekegn Heliso Dolla, Thabo Matthews, Usman Muhammad Tukur, Jiexi Wang, Nithyadharseni Palaniyandy, Nobanathi Wendy Maxakato, Peter Ramashadi Makgwane, Mkhulu Kenneth Mathe

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  14. Leveraging Machine Learning to Discover New Solid-State Materials: Topological Insulators, Semiconductors, And Solid Electrolytes Applications (Review Article)

    Jurnal MIPA dan Pembelajarannya · 2026-05-31

    Mohmammed Abdullah Mohammed, Anas A. Hamdi

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  15. Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

    InfoScience. · 2026-06-11

    T H Wang, Yu Meng, Tianhao Chen, Xiangfen Jiang, Yoshio Bando, Jinjue Zeng, Xuebin Wang

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  16. Interfacial charge screening and ion transport in defect-engineered transition metal oxides based anodes for high-rate multivalent ion storage

    Surfaces and Interfaces · 2026-06-14

    Emaan Shahid, Abdul Majid, Hira Azhar Cheema, Qurat-ul-Ain

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  17. Towards high-performance halide electrolytes: Data-driven approaches for materials design and synthesis

    Energy storage materials · 2026-06-14

    Zhihao Xing, Ying Luo, Wei Zheng, Junlin Wang, Quansheng Zhang, Zhuang Zheng, Jingying Xie, Baoyu Sun

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  18. High-entropy materials for electrochemical energy storage: Microstructure, phase evolution and performance in batteries and supercapacitors

    Journal of Alloys and Compounds · 2026-06-26

    Ali Abbas Khan, Gohar Mahboob, Dawood Majeed, Abid Aman, Kashif Nasaem

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  19. Machine learning-guided electrolyte–interface co-design for Li-based anode-free batteries: Toward a predictive framework for stable metal deposition

    Journal of Power Sources · 2026-06-27

    Hamidreza Namazi, Ashwini Kumar Pradhan, Robert Frischer, Ladislav Socha

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  20. Overcoming the Interface Bottleneck in Solid‐State Batteries: Electrolyte Design, Interface Engineering, and Computational Discovery

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  21. Bridging chemistry and composition-space exploration: Particle swarm optimization of multicomponent glassy sulfide electrolytes

    Journal of Power Sources · 2026-07-22

    Yujin Choi, Yujin Oh, Tapan Kumar Ghosh, Woon Bae Park, Kyunglim Pyo

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  22. Machine Learning and Theoretical Computation Synergy Advancing Halide Electrolytes Toward All‐Solid‐State Lithium Batteries: Recent Advances, Challenges, and Perspectives

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  23. Progress on Halide Solid State Electrolytes: Structural Regulation, Interfacial Engineering, and Machine Learning Driven Paradigms

    ACS Energy Letters · 2026-08-14

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  24. Machine learning for accelerated screening of sodium anode/chalcogenide electrolyte interfaces for all-solid-state sodium batteries

    Electrochimica Acta · 2026-08-18

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  25. Multiobjective Artificial Intelligence-Assisted High-Throughput Screening and Prioritization of Gel Polymer Electrolytes for Safer Lithium Batteries

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  26. Rational doping of oxide solid-state electrolytes for all-solid-state lithium metal batteries: Mechanisms and computational screening strategies

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  27. Unveiling the hidden effects of local potential energy surface on ion transport in superionic conductors

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  28. A Data‐to‐Interface Framework for AI‐Guided Sodium‐Ion Battery Materials Discovery: From Descriptors to Full‐Cell Decisions

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  29. Machine Learning‐Driven Design of Electrolytes and Interfaces for Solid‐State Lithium Batteries: Progress and Outlook

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  30. Review of AI-driven discovery of cathode anode and electrolyte materials for advancing sodium-ion batteries

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