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RESEARCH PAPER · Journal of Electroanalytical Chemistry · 2021

An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon

Peng Liu, Yangping Wen, Lei Huang, Xiaoyu Zhu, Ruimei Wu, Shirong Ai, Ting Xue, Yu Ge

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An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbonOriginalData-Driven Predictive Electrochemical Behavior of Lignin-Based Supercapacitors via Machine Learning46O and N co-doped porous carbon derived from crop waste for a high-stability all-solid-state symmetric supercapacitor47NiCo2O4 with unique 3D miniature sea urchins as binder-free electrode for high performance asymmetric supercapacitor23A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials17New engineering science insights into the electrodes pairing of electrochemical energy storage devices assisted by machine learning0An emerging machine learning strategy for electrochemical sensor and supercapacitor using carbonized metal–organic framework58Data-driven machine learning approach for predicting the capacitance of graphene-based supercapacitor electrodes98Analysis of impedance: The distribution of capacitance in halide ion treated supercapacitors7Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review119Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials36Electrospun Nitrogen Enriched Pei-Derived Carbon Nanofiber for Supercapacitors with Artificial Neural Network Modeling0Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66Astragalus brachycalyx fischer roots-derived porous carbon integrated with a novel NiSnO2/PC nanocomposite for high-performance supercapacitors3An Intelligent Model for Supercapacitors with a Graphene-Based Fractal Electrode to Investigate the Cyclic Voltammetry11Morphology tuned electrochemical properties of CuBO2 nanostructures10Modeling capacitance of carbon-based supercapacitors by artificial neural networks23Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization29“Crypton 1.0”: Accurate cyclic voltammetry forecasting of activated carbon electrode with machine learning2An artificial neural network model for capacitance prediction of porous carbon-based supercapacitor electrodes78Electrospun polyethylenimine (PEI)-derived nitrogen enriched carbon nanofiber for supercapacitors with artificial neural network modeling23Machine learning enabled property prediction of carbon-based electrodes for supercapacitors34Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes21Recent advances in metal-organic framework (MOF)-based agricultural sensors for metal ions: a review103A comprehensive review on MoSe 2 nanostructures with an overview of machine learning techniques for supercapacitor applications33Introduction to supercapacitors, materials and design2Leveraging machine learning in porous media89Sustainably transforming biomass into advanced carbon materials for solid-state supercapacitors: a review19Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology53Design of NiCo2O4 nanoarray morphology for optimizing electrochemical stability of supercapacitor7A comprehensive review on supercapacitors: Their promise to flexibility, high temperature, materials, design, and challenges270Recent advances in artificial intelligence boosting materials design for electrochemical energy storage124Data-Driven Optimization of Carbon Electrodes for Aqueous Supercapacitors10Modeling specific capacitance of carbon nanotube-based supercapacitor electrodes by machine learning algorithms11Machine Learning‐Guided Prediction of Desalination Capacity and Rate of Porous Carbons for Capacitive Deionization45Fabrication of an asymmetric supercapacitor using a novel electrode design and introduce a robust machine learning model for its performance evaluation25Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors32New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices20Calcination process of porous metal–organic frameworks derived from nickel sulfide composites for supercapacitor and computer vision for investigating the porosity-electrochemical correlation10Machine Learning‐Based Assessment and Optimization of Electrode Materials for Supercapacitors1Harnessing NaNH2 as a dual-function activator for synthesis of porous carbon and its impact on enhanced supercapacitor performance: a review5Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors20Recent trends of machine learning on energy storage devices7Ensemble Approach Assisted Specific Capacitance Prediction for Heteroatom‐Doped High‐Performance Supercapacitors4Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence34Advancements in graphene-based nanostructured conducting polymer hybrid composite electrodes for high-performance supercapacitors33Synergistic design and synthesis of O, N Co-doped hierarchical porous carbon for enhanced supercapacitor performance16Exploring how base model combination affects the results of a “stacking” ensemble machine learning model: An applied study on optimization of heteroatom doped carbon data8Data-based modeling for prediction of supercapacitor capacity: Integrated machine learning and metaheuristic algorithms8Research on prediction of energy density and power density of biomass carbon-based supercapacitors based on machine learning15Machine Learning Relationships Between Nanoporous Structures and Electrochemical Performance in MOF Supercapacitors52New insights into the performance of biomass carbon-based supercapacitors based on interpretable machine learning approach14Prediction of specific capacitance of activated carbon electrode for energy storage device by machine learning based approach19A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials85Machine Learning Enhanced Self‐Charging Power Sources21Review on Carbon Nanostructures for Supercapacitors: Cutting-Edge Energy Storage Applications and Perspectives18Machine learning-guided prediction of energy storage performance of carbon cathode materials for zinc-ion hybrid capacitors13Investigation of prediction approaches for the design and performance analysis of supercapacitors with biomass-based activated carbon electrodes13Machine learning-guided fabrication of NiO/CoO and CuO/CoO based nanocomposites for high-performance supercapacitors11Recent advances in the high entropy materials for advanced energy storage with machine learning6A review on nano-structured electrodes for high-performance supercapacitors: panoramic insights across dimensional spectra35Next-generation supercapacitors based on samarium nanoparticles: a review of material innovation and charge storage enhancement4Machine learning models for predicting electrochemical behavior of MXene for energy storage applications2Interpretable machine learning for optimizing the specific capacitance of biomass-derived supercapacitors4Machine learning applied to the optimization of biomass char-based supercapacitors: Effect of experimental parameters on supercapacitor performance3Metallurgical Indices for Physics-Informed Machine Learning-Based Fatigue Strength Prediction in Steels2ML empowered supercapacitors: A new era of predictive energy storage research4Machine learning-based energy density prediction of bio-based supercapacitor with Shapley additive model interpretation1Integrated design strategies for high-performance supercapacitors: a comprehensive review on the role of current collectors, electrode materials, electrolytes, and AI-driven optimization5Machine learning-assisted prediction of power density for bio-based supercapacitor1Machine Learning and Hybrid Fuzzy Logic for Prediction and Optimization of Flexible Supercapacitor Performance for Smart Textiles0Synthetic Porous Carbons for High-Energy, High-Power Supercapacitors4Machine learning-assisted QnSPR study of structurally diverse nanoporous carbon materials and their capacitive behavior in dilute ionic liquid electrolyte0Source-aware materials informatics for biomass-derived carbon electrodes: predicting supercapacitor capacitance from precursor and process descriptors0Toward Sustainable Desalination: Experimental Evaluation and Predictive Modeling of Membrane Capacitive Deionization1

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74 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Data-Driven Predictive Electrochemical Behavior of Lignin-Based Supercapacitors via Machine Learning

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  2. O and N co-doped porous carbon derived from crop waste for a high-stability all-solid-state symmetric supercapacitor

    New Journal of Chemistry · 2022-01-01

    Fengyu Wu, Xue Ren, Fenyang Tian, Guanghui Han, Jie Sheng, Yongsheng Yu, Yequn Liu, Weiwei Yang

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  3. NiCo2O4 with unique 3D miniature sea urchins as binder-free electrode for high performance asymmetric supercapacitor

    Journal of Electroanalytical Chemistry · 2022-01-28

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  4. A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials

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  5. New engineering science insights into the electrodes pairing of electrochemical energy storage devices assisted by machine learning

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  6. An emerging machine learning strategy for electrochemical sensor and supercapacitor using carbonized metal–organic framework

    Journal of Electroanalytical Chemistry · 2022-07-19

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  7. Data-driven machine learning approach for predicting the capacitance of graphene-based supercapacitor electrodes

    Journal of Energy Storage · 2022-08-06

    Ahmed G. Saad, Ahmed Emad-Eldeen, Wael Z. Tawfik, Ahmed G. El‐Deen

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  8. Analysis of impedance: The distribution of capacitance in halide ion treated supercapacitors

    Journal of Electroanalytical Chemistry · 2022-08-31

    Jinfeng Zhao, Georg Gorbatovski, Ove Oll, Thomas Thomberg, Enn Lust

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  9. Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review

    Journal of Energy Chemistry · 2022-11-16

    Vaishali A. Sawant, Rashmi Deshmukh, Chetan J. Awati

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  10. Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

    Energy Advances · 2023-01-01

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  11. Electrospun Nitrogen Enriched Pei-Derived Carbon Nanofiber for Supercapacitors with Artificial Neural Network Modeling

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    Burcu Üstün, Hamide Aydın, Serkan Naci Koç, Ahmet Uluslu, Ümran KURTAN

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  12. Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges

    Journal of Materials Chemistry A · 2023-01-01

    Swarn Jha, Matthew Yen, Yazmin Soto Salinas, Evan Palmer, John Anzel Villafuerte, Hong Yu Liang

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  13. Astragalus brachycalyx fischer roots-derived porous carbon integrated with a novel NiSnO2/PC nanocomposite for high-performance supercapacitors

    Journal of Materials Science Materials in Electronics · 2023-02-01

    Serkan Demirel, Mehmet Salih Nas, Adem Koçyiğit, Mehmet Harbi Çalımlı, Mehmet Hakkı Alma

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  14. An Intelligent Model for Supercapacitors with a Graphene-Based Fractal Electrode to Investigate the Cyclic Voltammetry

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    Nida Katı, Ferhat Uçar

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  15. Morphology tuned electrochemical properties of CuBO2 nanostructures

    Materials Chemistry and Physics · 2023-03-16

    Pulok Das, Nirmalya Sankar Das, Kausik Sardar, Brahami Das, Ashadul Adalder, Kalyan Kumar Chattopadhyay

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  16. Modeling capacitance of carbon-based supercapacitors by artificial neural networks

    Journal of Energy Storage · 2023-08-08

    B.S. Reddy, Pasupuleti Lakshmi Narayana, A.K. Maurya, Uma Maheshwera Reddy Paturi, Jaekyung Sung, Hyo‐Jun Ahn, K.K. Cho, N.S. Reddy

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  17. Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization

    Journal of Chemical Information and Modeling · 2023-08-10

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  18. “Crypton 1.0”: Accurate cyclic voltammetry forecasting of activated carbon electrode with machine learning

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  19. An artificial neural network model for capacitance prediction of porous carbon-based supercapacitor electrodes

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  20. Electrospun polyethylenimine (PEI)-derived nitrogen enriched carbon nanofiber for supercapacitors with artificial neural network modeling

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  21. Machine learning enabled property prediction of carbon-based electrodes for supercapacitors

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  22. Machine learning models for capacitance prediction of porous carbon-based supercapacitor electrodes

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  23. Recent advances in metal-organic framework (MOF)-based agricultural sensors for metal ions: a review

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  24. A comprehensive review on MoSe 2 nanostructures with an overview of machine learning techniques for supercapacitor applications

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  25. Introduction to supercapacitors, materials and design

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  27. Sustainably transforming biomass into advanced carbon materials for solid-state supercapacitors: a review

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  29. Design of NiCo2O4 nanoarray morphology for optimizing electrochemical stability of supercapacitor

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  30. A comprehensive review on supercapacitors: Their promise to flexibility, high temperature, materials, design, and challenges

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  32. Data-Driven Optimization of Carbon Electrodes for Aqueous Supercapacitors

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  33. Modeling specific capacitance of carbon nanotube-based supercapacitor electrodes by machine learning algorithms

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  34. Machine Learning‐Guided Prediction of Desalination Capacity and Rate of Porous Carbons for Capacitive Deionization

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  35. Fabrication of an asymmetric supercapacitor using a novel electrode design and introduce a robust machine learning model for its performance evaluation

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  36. Machine Learning‐Assisted Research and Development of Chemiresistive Gas Sensors

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  38. Calcination process of porous metal–organic frameworks derived from nickel sulfide composites for supercapacitor and computer vision for investigating the porosity-electrochemical correlation

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  39. Machine Learning‐Based Assessment and Optimization of Electrode Materials for Supercapacitors

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  40. Harnessing NaNH2 as a dual-function activator for synthesis of porous carbon and its impact on enhanced supercapacitor performance: a review

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  41. Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors

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  42. Recent trends of machine learning on energy storage devices

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  43. Ensemble Approach Assisted Specific Capacitance Prediction for Heteroatom‐Doped High‐Performance Supercapacitors

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  44. Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence

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  45. Advancements in graphene-based nanostructured conducting polymer hybrid composite electrodes for high-performance supercapacitors

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  46. Synergistic design and synthesis of O, N Co-doped hierarchical porous carbon for enhanced supercapacitor performance

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  47. Exploring how base model combination affects the results of a “stacking” ensemble machine learning model: An applied study on optimization of heteroatom doped carbon data

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  48. Data-based modeling for prediction of supercapacitor capacity: Integrated machine learning and metaheuristic algorithms

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  49. Research on prediction of energy density and power density of biomass carbon-based supercapacitors based on machine learning

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  50. Machine Learning Relationships Between Nanoporous Structures and Electrochemical Performance in MOF Supercapacitors

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