BetaCitation Atlas

RESEARCH PAPER · Materials · 2022

Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte

Qianyu Hu, Kunfeng Chen, Fei Liu, Mengying Zhao, Feng Liang, Dongfeng Xue

Read original paper ↗

See where this research leads.

33 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State ElectrolyteOriginalModelling of functional properties of shape-memory alloys by machine learning methods4High‐Throughput Data‐Driven Prediction of Stable High‐Performance Na‐Ion Sulfide Solid Electrolytes24Polymeric interface engineering in lithium-sulfur batteries18Strain effects on lithium ion diffusion in various crystal structures9Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Data-Driven Prognosis of Multiscale and Multiphysics Complex System Anomalies: Its Application to Lithium-ion Batteries Failure Detection31Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries115KE: A Knowledge Enhancing Framework for Machine Learning Models3Material Type Prediction Using Machine Learning Techniques1Machine learning guided design of mechanically efficient metamaterials with auxeticity22Liquid Crystal Orientation and Shape Optimization for the Active Response of Liquid Crystal Elastomers8Speeding up the development of solid state electrolyte by machine learning22Data refinement for enhanced ionic conductivity prediction in garnet-type solid-state electrolytes12A Material Type Prediction Model Based on Machine Learning Technology0Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning9Components of smart materials0Enhancing lithium-ion battery performance with emerging electrolyte materials for sustainable energy storage solutions: A comprehensive review and prospects16Recent Applications of Theoretical Calculations and Artificial Intelligence in Solid-State Electrolyte Research: A Review39Application-oriented design of machine learning paradigms for battery science69Machine Learning-Driven Design of Smart Materials with Adaptive Properties for Advanced Applications2Predicting ionic conductivity in solids from the machine-learned potential energy landscape17Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation48Photoactivated Nano-Compatibilized Two-Phase Polymer Blends: An Approach for Determining Mechanical Behavior0Covalent organic framework-based solid polymer electrolytes for metal-ion batteries: pioneering the future of DFT, MD, and ML techniques4Convergence of Computational Materials Science and AI for Next-Generation Energy Storage Materials6Advanced Polymer Materials: Synthesis, Characterizations and Applications1Deconstruction Analysis and Regeneration Optimization of Solid Electrolyte Components for Energy Storage Based on the Finite Data Statistics0Data-driven inorganic material design for future all-solid-state batteries: Innovations and perspectives2Emerging materials for next-generation energy storage: A roadmap to solid-state and beyond7Toward data-driven lithium metal batteries: Machine learning for materials design, mechanistic insight, and autonomous optimization0Artificial intelligence for energy storage systems: State of the art and future perspectives1Topological machine learning and optimization for discovery lithium-ion conducting crystals for solid-state electrolytes0Artificial intelligence-assisted investigation of ion transport mechanisms and material screening for solid-state electrolytes0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

33 direct citing papers saved.

  1. Modelling of functional properties of shape-memory alloys by machine learning methods

    Scientific journal of the Ternopil national technical university · 2022-01-01

    Oleh Yasniy, Vladyslav Demchyk, Nadiia Lutsyk

    Read original paper ↗ ·

  2. High‐Throughput Data‐Driven Prediction of Stable High‐Performance Na‐Ion Sulfide Solid Electrolytes

    Advanced Functional Materials · 2022-09-16

    Seong‐Hoon Jang, Yoshitaka Tateyama, Randy Jalem

    Read original paper ↗ ·

  3. Polymeric interface engineering in lithium-sulfur batteries

    Chemical Engineering Journal · 2022-11-16

    Zhongfeng Ji, Lanxiang Feng, Zhiwei Zhu, Xuewei Fu, Wei Min Yang, Yu Wang

    Read original paper ↗ ·

  4. Strain effects on lithium ion diffusion in various crystal structures

    Physical Chemistry Chemical Physics · 2023-01-01

    Bicong Liu, Jiamin Guo, Xiao Ying Gu

    Read original paper ↗ ·

  5. Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization

    Energy storage materials · 2023-04-23

    Liqianyun Xu, Feng Wu, Renjie Chen, Li Li

    Read original paper ↗ ·

  6. Data-Driven Prognosis of Multiscale and Multiphysics Complex System Anomalies: Its Application to Lithium-ion Batteries Failure Detection

    Journal of The Electrochemical Society · 2023-05-01

    Lin Liu

    Read original paper ↗ ·

  7. Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries

    Nature Communications · 2023-05-15

    Kai Li, Jifeng Wang, Yuanyuan Song, Ying Wang

    Read original paper ↗ ·

  8. KE: A Knowledge Enhancing Framework for Machine Learning Models

    The Journal of Physical Chemistry A · 2023-09-29

    Yijue Wang, Nidhibahen Shah, Ahmed T. Soliman, Dan Guo, Sanguthevar Rajasekaran

    Read original paper ↗ ·

  9. Material Type Prediction Using Machine Learning Techniques

    International Journal of Science and Research (IJSR) · 2024-02-05

    Debmalya Ray

    Read original paper ↗ ·

  10. Machine learning guided design of mechanically efficient metamaterials with auxeticity

    Materials Today Communications · 2024-04-15

    Qing Zhou, Aiguo Zhao, Han Wang, Chuang Liu

    Read original paper ↗ ·

  11. Liquid Crystal Orientation and Shape Optimization for the Active Response of Liquid Crystal Elastomers

    Polymers · 2024-05-17

    Jorge Barrera, Caitlyn C. Cook, Elaine Lee, Kenneth E. Swartz, Daniel A. Tortorelli

    Read original paper ↗ ·

  12. Speeding up the development of solid state electrolyte by machine learning

    Next Energy · 2024-06-26

    Qianyu Hu, Kunfeng Chen, Jinyu Li, Tingting Zhao, Feng Liang, Dongfeng Xue

    Read original paper ↗ ·

  13. Data refinement for enhanced ionic conductivity prediction in garnet-type solid-state electrolytes

    Solid State Ionics · 2024-10-11

    Zakaria Kharbouch, Mustapha Bouchaara, Fadila El Kouihen, Abderrahmane Habbal, Ahmed Ratnani, Abdessamad Faik

    Read original paper ↗ ·

  14. A Material Type Prediction Model Based on Machine Learning Technology

    Journal of Research in Science and Engineering · 2024-11-29

    Seetaram Nagaraju Naik

    Read original paper ↗ ·

  15. Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning

    Journal of Chemical Information and Modeling · 2024-12-12

    Zixin Zhuang, Amanda Susan Barnard

    Read original paper ↗ ·

  16. Components of smart materials

    Elsevier eBooks · 2025-01-01

    Neha, Pooja Gupta, Ruchika Kaushik

    Read original paper ↗ ·

  17. Enhancing lithium-ion battery performance with emerging electrolyte materials for sustainable energy storage solutions: A comprehensive review and prospects

    Unknown journal · 2025-01-01

    Nitin Liladhar Rane, Saurabh Choudhary, Jayesh Rane

    Read original paper ↗ ·

  18. 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

    Read original paper ↗ ·

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

    npj Computational Materials · 2025-04-02

    Ying Wang

    Read original paper ↗ ·

  20. Machine Learning-Driven Design of Smart Materials with Adaptive Properties for Advanced Applications

    Unknown journal · 2025-04-07

    M T Vasumathi, V Asha, N S Sukanya, Bhatt Vatsal Shailesh Kumar, Ayush Raj, Basavaraj Basavaraj

    Read original paper ↗ ·

  21. Predicting ionic conductivity in solids from the machine-learned potential energy landscape

    Physical Review Research · 2025-05-19

    A. Maevskiy, Alexandra Carvalho, Emil Sataev, Volha Turchyna, Keian Noori, Aleksandr S. Rodin, A. H. Castro Neto, A. Ustyuzhanin

    Read original paper ↗ ·

  22. Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation

    Nano-Micro Letters · 2025-06-06

    Sheng Wang, Jin-Cheng Liu, Xiaopan Song, Huajian Xu, Yang Gu, Junyu Fan, Bin Sun, Linwei Yu

    Read original paper ↗ ·

  23. Photoactivated Nano-Compatibilized Two-Phase Polymer Blends: An Approach for Determining Mechanical Behavior

    The Journal of Physical Chemistry B · 2025-06-28

    Surbhi Khewle, Pratyush Dayal

    Read original paper ↗ ·

  24. Covalent organic framework-based solid polymer electrolytes for metal-ion batteries: pioneering the future of DFT, MD, and ML techniques

    Energy storage materials · 2025-10-01

    Sadegh Kaviani

    Read original paper ↗ ·

  25. Convergence of Computational Materials Science and AI for Next-Generation Energy Storage Materials

    Journal of Electronic Materials · 2025-11-21

    Xuexue Pan, Yuanlin Xie, Chujun Li, Yangyang He, Yitong Zhang, Yilin Wang, Zhichun Li, Peiyu Peng, Jun Wang

    Read original paper ↗ ·

  26. Advanced Polymer Materials: Synthesis, Characterizations and Applications

    Springer eBooks · 2026-01-01

    Wen Liu, Pilan Zhang, Zhiqi Chen, Yuwen Zeng, Hai Qian, Yifei Xu, Xueyan Feng, Yue Gao, Ying Wang

    Read original paper ↗ ·

  27. Deconstruction Analysis and Regeneration Optimization of Solid Electrolyte Components for Energy Storage Based on the Finite Data Statistics

    ACS Omega · 2026-01-07

    Boyuan Jian, Yatong Zhen, Yunxi Yang, Junhong Hao, Xiaoze Du

    Read original paper ↗ ·

  28. 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

    Read original paper ↗ ·

  29. Emerging materials for next-generation energy storage: A roadmap to solid-state and beyond

    Next Energy · 2026-03-26

    Avadhesh Kumar Yadav

    Read original paper ↗ ·

  30. Toward data-driven lithium metal batteries: Machine learning for materials design, mechanistic insight, and autonomous optimization

    Energy storage materials · 2026-05-24

    Yu Lou, Hongyang Zhao, J Li, Wenxuan Xu, Chaocang Weng, J Li, Likun Pan

    Read original paper ↗ ·

  31. Artificial intelligence for energy storage systems: State of the art and future perspectives

    Materials Science in Semiconductor Processing · 2026-07-02

    Yedluri Anil Kumar, Tholkappiyan Ramachandran, Laxman Singh, Mani Durai, Kwun Nam Hui, Akhilesh Kumar Yadav, Duc Anh Dinh

    Read original paper ↗ ·

  32. Topological machine learning and optimization for discovery lithium-ion conducting crystals for solid-state electrolytes

    Next Materials · 2026-08-12

    Juan de Anda-Suárez, Germán Pérez-Zúñiga, José Luis López-Ramírez, Juan Gabriel Avina‐Cervantes, Rosa Trasviña-Osorio, Gabriel Herrera‐Pérez, Luis Fernando Villanueva-Jiménez

    Read original paper ↗ ·

  33. Artificial intelligence-assisted investigation of ion transport mechanisms and material screening for solid-state electrolytes

    Energy storage materials · 2026-09-02

    Xuhui Wang, Shaoqiang Niu, Dongze Li, Zhuan Wang, Hongwei Zhou, Xufeng Dong, Yao Yao

    Read original paper ↗ ·
Citation Atlas · A map of research, not a ranking.