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RESEARCH PAPER · Journal of Materials Chemistry A · 2023

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

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

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Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challengesOriginalSelf-Supported Porous Polymetallic Tungstates Core-Shell Nanorod Arrays for High-Performance Hybrid Supercapacitors0Machine Learning Validation via Rational Dataset Sampling with astartes17Machine Learning-Assisted Electrode Material Fabrication and Electrochemical Efficiency Prediction and Validation of PANI-Ni/Co Hydroxide Nanocomposites31A comprehensive review on MoSe 2 nanostructures with an overview of machine learning techniques for supercapacitor applications33Exchanging interlayer anions in NiFe-LDHs nanosphere enables superior battery-type storage for high-rate aqueous hybrid supercapacitors59Future Perspectives and Emerging Trends in Nanomaterials for Energy Storage2In-depth understanding of electrochemical energy storage efficiency in a series of new 3d–4d mixed metal polyoxometalates: experimental and theoretical investigations15Insights on the rate performance of polyaniline supercapacitors by integrated mathematical modeling and machine learning1Leveraging machine learning in porous media89Synthesis and applications of self-supported porous polymetallic tungstates heterostructure electrodes for high-performance asymmetric supercapacitors14Progress in flexible supercapacitors for wearable electronics using graphene-based organic frameworks81Attention towards chemistry agnostic and explainable battery lifetime prediction24Predicting the Remaining Useful Life of Supercapacitors under Different Operating Conditions56A Novel Supercapacitor Degradation Prediction Using a 1D Convolutional Neural Network and Improved Informer Model35Architectural engineering of nanocomposite electrodes for energy storage3Estimating state of charge of cylindrical lithium-ion cells using multiple random convolutional kernel transform and low-frequency stress waves11Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors20Efficient structure-informed featurization and property prediction of ordered, dilute, and random atomic structures9Next-Gen Energy Storage Devices1A mixed-ligand approach to a cobalt-based electroactive framework for superior supercapacitor performance5AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics11Capacitance Classification for Supercapacitors Using Machine Learning4Data-driven finding of organic anode active materials for lithium-ion batteries from natural flower scent products using capacity predictors2Nickel cobalt phosphate/phosphide as a promising electrode material for extrinsic supercapacitors: machine learning analysis41Ion beam engineering of nanostructured materials for superior performance in supercapacitor applications: Recent progress and future perspectives11Synergistic design and synthesis of O, N Co-doped hierarchical porous carbon for enhanced supercapacitor performance16Transition Metal Molybdates Emerging Materials for High‐Performance Supercapacitors: A Machine Learning Analysis33Temperature-dependent performance prediction for cerium oxynitride solid-state symmetric supercapacitor using machine learning11Machine Learning Enhanced Self‐Charging Power Sources21Energy storage in supercapacitor researches: Interdisciplinary applications from molecular simulations to machine learning15Advancements on the synthesis and modification of metal-organic framework derivatives for supercapacitors15Calculated solvation and ionization energies for thousands of organic molecules relevant to battery design1From data to device: Machine learning-driven optimization, predictions, and experimental insights for engineering high-performance MnO2 supercapacitors12Engineering novel sea urchin-like bimetallic cu co selenide nanostructures for high-performance asymmetric supercapacitors5Artificial Intelligence in Predicting Performance of Sustainable Marine Materials0Recent advances in the high entropy materials for advanced energy storage with machine learning6Evaluating machine learning models for predicting the storage performance of polypyrrole-based supercapacitor electrodes9Hybrid MXenes for supercapacitors: Advances, mechanisms, and outlook3Machine learning-guided optimization, predictive modeling, and experimental validation of MXene-based supercapacitors13Convergence of Computational Materials Science and AI for Next-Generation Energy Storage Materials6A review on nano-structured electrodes for high-performance supercapacitors: panoramic insights across dimensional spectra35AI‐Driven Optimization of Waste‐Derived Nanocomposites for High‐Performance Microbial Fuel Cells4Toward efficient dielectric material discovery: a comparative evaluation of Transformer and AutoML-based inverse learning1Artificial intelligence-driven electrolyte chemistry for lithium-ion batteries: mechanisms, progress and strategic perspectives0Data-Driven Machine Learning Approach for Predicting the Capacitance of ZnCo2O4 -Based Supercapacitor Electrodes0Data-Driven Machine Learning Approach for Predicting the Capacitance of ZnCo2O4 -Based Supercapacitor Electrodes0Designing High-Performance Gel Polymer Electrolytes for Supercapacitors: A Combined DFT and Machine Learning Approach to Ionic Liquid-PVDF Complexes0Electrode Materials and Prediction of Cycle Stability and Remaining Service Life of Supercapacitors29Integrated Experimental and Machine Learning for Optimization and Performance Prediction of Cobalt-Based Metal-Organic Framework-Coated Nickel Foam Electrodes for Symmetric Supercapacitors0Recent advances in carbon-based materials for supercapacitors: synthesis, electrochemical properties, and impact of machine learning12Recent progress in supercapacitors: A critical review of energy storage mechanisms, electrode design strategies and future perspectives19Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation4Accelerating supercapacitor lifetime optimization using quantum machine learning surrogates0Prediction of Composite Supercapacitor Performance Through Combining Machine Learning with Novel Binder-Related Features1Synergizing machine learning and self-assembly to hypercrosslinked polymers-based carbon nanotubes for supercapacitors0Quantum-artificial intelligence framework for suppressing dendrites and enhancing interface stability in lithium-metal solid-state batteries0AI-driven sustainable supercapacitors for advanced renewable energy storage applications1Flexible and Wearable Carbon Cloth‐Based Supercapacitors: Synthesis, Applications, and Challenges1Machine learning-assisted QnSPR study of structurally diverse nanoporous carbon materials and their capacitive behavior in dilute ionic liquid electrolyte0Machine learning driven synthesis of nickel phosphate for advanced hybrid supercapacitors0Solvent- and binder-free PEEK–LIG electrodes for supercapacitors with data-driven RUL prediction0Applications and prospects of machine learning in aqueous batteries0A mini review on the role of artificial intelligence in direct methanol fuel cells: Toward enhanced efficiency and sustainability0Reliability-by-design for deployable next-generation supercapacitors: degradation, harsh-environment operation, sustainability, and AI-guided lifetime prediction0A novel hierarchical optimal control strategy for hybrid energy storage considering smoothing wind power fluctuations and compensating prediction errors0From Trial-and-Error to Closed-Loop Design: A Data-Driven Review on Biomass-Derived Carbon for Supercapacitors0

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  2. Machine Learning Validation via Rational Dataset Sampling with astartes

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  3. Machine Learning-Assisted Electrode Material Fabrication and Electrochemical Efficiency Prediction and Validation of PANI-Ni/Co Hydroxide Nanocomposites

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

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  5. Exchanging interlayer anions in NiFe-LDHs nanosphere enables superior battery-type storage for high-rate aqueous hybrid supercapacitors

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  6. Future Perspectives and Emerging Trends in Nanomaterials for Energy Storage

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  8. Insights on the rate performance of polyaniline supercapacitors by integrated mathematical modeling and machine learning

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  10. Synthesis and applications of self-supported porous polymetallic tungstates heterostructure electrodes for high-performance asymmetric supercapacitors

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  13. Predicting the Remaining Useful Life of Supercapacitors under Different Operating Conditions

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  14. A Novel Supercapacitor Degradation Prediction Using a 1D Convolutional Neural Network and Improved Informer Model

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  15. Architectural engineering of nanocomposite electrodes for energy storage

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  20. A mixed-ligand approach to a cobalt-based electroactive framework for superior supercapacitor performance

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  25. Ion beam engineering of nanostructured materials for superior performance in supercapacitor applications: Recent progress and future perspectives

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

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