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RESEARCH PAPER · Journal of Energy Chemistry · 2020

Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

Yongqiang Kang, Lejing Li, Baohua Li

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Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistryOriginalRecent Advances of Producing Biobased N‑Containing Compounds via Thermo-Chemical Conversion with Ammonia Process49Training Model for Predicting Adsorption Energy of Metal Ions Based on Machine Learning5Machine learning for advanced energy materials208Heterogeneous, Defect-Rich Battery Particles and Electrodes: Why Do They Matter, and How Can One Leverage Them?14Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale166Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale56Machine Learning Force Fields: Recent Advances and Remaining Challenges127Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage260Accurate machine learning models based on small dataset of energetic materials through spatial matrix featurization methods38Predicting the CME arrival time based on the recommendation algorithm11Effect of functional modification of porous medium on phase change behavior and heat storage characteristics of form-stable composite phase change materials: A critical review41A mini-review of artificial intelligence techniques for predicting the performance of supercapacitors26Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm707Text Mining for Energy Materials3Predicting the Heat of Hydride Formation by Graph Neural Network ‐ Exploring the Structure–Property Relation for Metal Hydrides23A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries288A review on the current research on microwave processing techniques applied to graphene-based supercapacitor electrodes: An emerging approach beyond conventional heating251A Data‐Driven framework for prediction the cyclic voltammetry and polarization curves of polymer electrolyte fuel cells using artificial neural networks4Comprehensive recycling of lithium-ion batteries: Fundamentals, pretreatment, and perspectives248Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review119An integrated machine learning model for accurate and robust prediction of superconducting critical temperature28Accelerating the development of electrocatalysts for electrochemical nitrogen fixation through theoretical and computational approaches11Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66The Application of Novel Functional Materials to Machine Learning0Analysis and evaluation of machine learning applications in materials design and discovery33Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks15Unfolding the structure-property relationships of Li2S anchoring on two-dimensional materials with high-throughput calculations and machine learning18Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives241Auxiliary guidance manufacture and revealing potential mechanism of perovskite solar cell using machine learning18Atom substitution of the solid-state electrolyte Li10GeP2S12 for stabilized all-solid-state lithium metal batteries28Neural Network Models for Estimating the Impact of Physicochemical and Operational Parameters on the Specific Capacity of Activated-Carbon-Based Supercapacitors3Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation49Machine learning in the era of smart automation for renewable energy materials9Uncovering the active sites of single atom-doped rutile oxides during methane activation by data-driven approach11Machine Learning-Based Prediction of the Excitation Wavelength of Phosphors2The application of machine learning models based on feature selection for the prediction of the superconducting critical temperature0Predictive models for inorganic materials thermoelectric properties with machine learning5Machine Learning‐Based Assessment and Optimization of Electrode Materials for Supercapacitors1Cross-Modal Prediction of Spectral and Structural Descriptors via a Pretrained Model Enhanced with Chemical Insights18Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74TH-graphyne: a new porous bidimensional carbon allotrope22Repairing Noise-Contaminated Low-Frequency Vibrational Spectra with an Attention U-Net10Artificial intelligence in rechargeable battery: Advancements and prospects37Machine learning in advancing anode materials for Lithium-Ion batteries – A review17A SYSTEMATIC LITERATURE REVIEW ON ENERGY-EFFICIENT TRANSFORMER DESIGN FOR SMART GRIDS0Enabling high-performance multivalent metal-ion batteries: current advances and future prospects146Computational understanding and multiscale simulation of secondary batteries14Leveraging machine learning for accelerated materials innovation in lithium-ion battery: A review54Enhancing energy materials with data-driven methods: A roadmap to long-term hydrogen energy sustainability using machine learning16Considerations in the use of machine learning force fields for free energy calculations3Machine learning applications in energy systems: current trends, challenges, and research directions91Energy storage in supercapacitor researches: Interdisciplinary applications from molecular simulations to machine learning15Machine learning-assisted prediction of ionic conductivity in doped LiTi2(PO4)3 solid electrolytes8Prediction and Interpretability Study of the Glass Transition Temperature of Polyimide Based on Machine Learning and Molecular Dynamics Simulations8Machine learning-based investigation of δ phase precipitation behavior in IN718 alloy and its influence on tensile properties8Electrostatic potential distribution image-transfer learning method for highly accurate prediction of lithium diffusion barriers on transition metal dichalcogenide surfaces0Decoding the ZT optimization of BiCuSeO thermoelectrics via interpretable machine learning4An automated workflow for lithium metal oxide screening assisted by large language models0Machine learning for renewable energy advancements: Prospects and emerging techniques12Machine Learning in Multispectral Compatible Camouflage: From Traditional Materials to Intelligent Design0Machine learning in sodium-ion battery development: Critical perspectives on design innovation and applications2Interpretable machine learning for tensile strength prediction and processing optimization of thermoplastic vulcanizates0

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