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RESEARCH PAPER · Energy and AI · 2021

Machine learning for advanced energy materials

Liu Yun, Oladapo Christopher Esan, Zhefei Pan, Liang An

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Machine learning for advanced energy materialsOriginalA computational model of a liquid e-fuel cell12Boosting electrocatalytic nitrogen reduction to ammonia in alkaline media8Predictive modelling for contact angle of liquid metals and oxide ceramics by comparing Gaussian process regression with other machine learning methods23Correlation between microstructures and macroscopic properties of nickel/yttria-stabilized zirconia (Ni-YSZ) anodes: Meso-scale modeling and deep learning with convolutional neural networks50Novel Materials for Urban Farming91Performance of CO2 electrolysis using solid oxide electrolysis cell with Ni-YSZ as fuel electrode under different fuel atmospheres18High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications107Automated synthesis and data accumulation for fast production of high-performance Ni nanocatalysts10Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence79Sm-Co ALLOYS COERCIVITY PREDICTION USING STACKING HETEROGENEOUS ENSEMBLE MODEL8Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches0Rational design of high power density “Blue Energy Harvester” pressure retarded osmosis (PRO) membranes using artificial intelligence-based modeling and optimization20Data-driven approach towards identifying dyesensitizer molecules for higher power conversion efficiency in solar cells19Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors39Machine learning for design principles for single atom catalysts towards electrochemical reactions116Role of Computational Science in Materials and Systems Design for Sustainable Energy Applications: An Industry Perspective8Fuel sorption into polymers: Experimental and machine learning studies13Dissimilar welding and brazing of zirconium and its alloys: Methods, parameters, metallurgy and properties of joints38UTILIZAÇÃO DE TÉCNICAS DE CLASSIFICAÇÃO EM CONJUNTO DE DADOS SOBRE INCLUSÃO FINANCEIRA: UM ESTUDO BASEADO EM PAÍSES LATINOAMERICANOS0Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm707Structure–Activity Relationships in Lewis Acid–Base Heterogeneous Catalysis88Dynamic modeling of Pt degradation and mitigation strategies in polymer electrolyte membrane fuel cells63Transfer learning using attentions across atomic systems with graph neural networks (TAAG)34Recent progress towards a universal machine learning model for reaction energetics in heterogeneous catalysis20Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys21Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break15Quasi‐solid‐state conversion cathode materials for room‐temperature sodium–sulfur batteries28Molecular Modeling in Anion Exchange Membrane Research: A Brief Review of Recent Applications29Accelerating the theoretical study of Li‐polysulfide adsorption on single‐atom catalysts via machine learning approaches7Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle93Green ammonia as a fuel47Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model11Review on Applications of Machine Learning in Coastal and Ocean Engineering58Generalized Spatial–Temporal Fault Location Method for Solid Oxide Fuel Cells Using LSTM and Causal Inference28Data-Driven Lithium-Ion Battery Cathode Research with State-of-the-Art Synchrotron X-ray Techniques23Enhanced oxygen discharge with structured mesh channel in proton exchange membrane electrolysis cell93Machine-learning-assisted insight into the cathode catalyst layer in proton exchange membrane fuel cells42Advanced Cathode Materials for Protonic Ceramic Fuel Cells: Recent Progress and Future Perspectives183Prediction of the permeability of fibrous porous structures under the full flow regimes8Evaluation of Machine Learning Models on Electrochemical CO2 Reduction Using Human Curated Datasets35A Review of Performance Prediction Based on Machine Learning in Materials Science61Full-scale three-dimensional simulation of air-cooled proton exchange membrane fuel cell stack: Temperature spatial variation and comprehensive validation91Toward a Li‐Ion Battery Ontology Covering Production and Material Structure21Materials discovery of ion-selective membranes using artificial intelligence30Performance Characteristics of a Direct Ammonia Fuel Cell with an Anion Exchange Membrane57Machine Learning a Million Cycles as 2D Images from Practical Batteries for Electric Vehicle Applications11Machine learning accelerates the materials discovery142Toward a function realization of multi‐scale modeling for lithium‐ion battery based on CHAIN framework19Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review119Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges274Computational and Data-Driven Materials Engineering: Multimodal Materials Datasets, Integration Frameworks, and Discovery Potential0Computational and data-driven modelling of solid polymer electrolytes21Machine learning toward improving the performance of membrane-based wastewater treatment: A review53Machine learning-inspired battery material innovation49Perovskite-Based Materials for Photovoltaic Applications: A Machine Learning Approach0The Application of Novel Functional Materials to Machine Learning0Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management0Machine learning approach to understanding the ‘synergistic’ pseudocapacitive effects of heteroatom doped graphene27Investigation on the reaction mechanism of solid oxide co-electrolysis with different inlet mixtures based on the comparison of CO2 electrolysis and H2O electrolysis40Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: A Review151Machine learning assisted advanced battery thermal management system: A state-of-the-art review117Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27Analysis and evaluation of machine learning applications in materials design and discovery33Advanced ocean wave energy harvesting: current progress and future trends54Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis—Challenges and Opportunities7Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning34A review on artificial roughened solar air heaters with and without thermal energy storage9Environmental life cycle assessment of biomass conversion using hydrothermal technology: A review109Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering65A systematic review of machine learning approaches in carbon capture applications66Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Arcing Stabilization Effect and Ablation Characteristics for C–Cu Electrodes at Low Air Pressure13AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122The design of compositionally modulated lead-free ferroelectrics with large electromechanical response via high-throughput phase-field simulations and machine learning9Applications of machine learning in supercritical fluids research47Dye sensitization of TiO2 relevant to photocatalytic hydrogen generation: Current research trends and prospects115Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review191Batteries temperature prediction and thermal management using machine learning: An overview132Customer Churn Prediction of Telecom Company Using Machine Learning Algorithms9Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction207A comprehensive review of artificial intelligence and machine learning applications in energy consumption and production52A critical review of future aspects of digitalization next generation Li-ion batteries manufacturing process38Machine learning assisted screening of doped metals phosphides electrocatalyst towards efficient hydrogen evolution reaction19AI-enabled materials discovery for advanced ceramic electrochemical cells28A perspective on data-driven screening and discovery of polymer membranes for gas separation, from the molecular structure to the industrial performance11Advances of machine learning in materials science: Ideas and techniques105Predicting satisfaction with democracy in Brazil considering data form an opinion survey1Solid-liquid phase transition temperature prediction of alloys based on machine learning key feature screening5A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing2Application of Artificial Intelligence in Biochemistry Research: A Review1A Meta-Analysis of Electricity Demand Forecasting Using Modelling Methods, Factors, and Horizon: A Review Study of Australia, Pakistan, and Sri Lanka0Insights on the rate performance of polyaniline supercapacitors by integrated mathematical modeling and machine learning1Machine learning and artificial intelligence-distributed renewable energy sources: technologies, perspectives, and challenges7Nature-inspired and computer-aided approaches to enable improved photovoltaic materials, more efficient processing, and novel devices0Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning1Progress of machine learning in materials design for Li-Ion battery19Recent Development, Challenges, and Breakthroughs of Thermochemical Conversion Technologies0Advances in materials and machine learning techniques for energy storage devices: A comprehensive review65Advances and challenges in high-performance cathodes for protonic solid oxide fuel cells and machine learning-guided perspectives57Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry93Artificial intelligence and corporate carbon neutrality: A qualitative exploration82Advances in Methane Emission Estimation in Livestock: A Review of Data Collection Methods, Model Development and the Role of AI Technologies69Coordinated development of rural ecological construction and carbon neutrality: a deep learning approach for enhanced sustainability0Controlled Synthesis of Space–Time Modulated Metamaterial for Enhanced Nonreciprocity by Machine Learning4Machine learning in the era of smart automation for renewable energy materials9Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology53Rational electrode design for low-cost proton exchange membrane water electrolyzers33Machine learning-assisted design of low elastic modulus β-type medical titanium alloys and experimental validation27Methodology for Studying Combustion of Solid Rocket Propellants using Artificial Neural Networks3Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries11Machine learning accelerates the screening of single-atom catalysts towards CO2 electroreduction30Machine learning for CO 2 capture and conversion: A review60How Machine Learning is Impacting Energy Production from Biomass: A Systematic Review and Multiple Case Study6Exploring solvation structure and transport behavior for rational design of advanced electrolytes for next generation of lithium batteries15Applications and potential of advanced materials: An overview14Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018–2023)52Response surface methodology and machine learning optimisations comparisons of recycled AA6061-B4C–ZrO2 hybrid metal matrix composites via hot forging forming process13Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery29High entropy oxides for reversible lithium-ion battery: a brief review4Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space32Applying Machine Learning to Design Delicate Amorphous Micro-Nano Materials for Rechargeable Batteries20Synergetic enhancement of hydrogen concentration and heat transfer for triply periodic minimal surface based hydrogen storage reactor16Predictive models for inorganic materials thermoelectric properties with machine learning5Advanced ceramics in energy storage applications: Batteries to hydrogen energy58Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials28Comprehensive review of AI and ML tools for earthquake damage assessment and retrofitting strategies19Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm29Comparison of Methane Emissions from Dairy Cows Quantified by Maximum Amplitude of Eructation Peaks and other Concentration Metrics0Machine Learning‐Based Assessment and Optimization of Electrode Materials for Supercapacitors1A review on machine learning-guided design of energy materials19Overcoming the Barriers to Machine Learning Adoption in External Auditing: Insights from Auditors' Perspectives1Survey of Machine Learning Applications in Wind Power Implementation: A Comprehensive View1Metal-oxide nanocatalysts for spontaneous sequestration of endocrine-disrupting compounds from wastewater13Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data22Machine learning in advancing anode materials for Lithium-Ion batteries – A review17Creep Fracture Life Prediction of 9%Cr Heat‐Resistant Steel Based on the Back‐Propagation Artificial Neural Network Considering Microstructure Characteristics0Machine Learning-Driven Advances in Metal-Organic Framework Nanomaterials for Wastewater Treatment: Developments and Challenges27Advancements in biomass-derived cellulose composite electrodes for supercapacitors: a review31Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives92The use of artificial neural networks in creating ceramic and refractory materials0Data-driven atomistic modeling of crystalline and glassy solid-state electrolytes6Development of Artificial Intelligence Based Advanced Battery Management System with Enhanced Charging Monitoring and Fire Protection for Electric Vehicles3Integrating artificial intelligence in energy transition: A comprehensive review248Large language models for knowledge graph extraction from tables in materials science12Reducing the Financial Burden of Typhoid Fever Treatment Through Intelligent Modeling1Screening of potential candidates for solid electrolyte interphase materials for lithium-ion batteries through a data-driven approach5Transforming Carbon-Based Material: The Role of AI and ML Regression Techniques in Material Science0Application of machine learning in polyimide structure design and property regulation5Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research9Application of machine learning in adsorption energy storage using metal organic frameworks: A review17

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  2. Boosting electrocatalytic nitrogen reduction to ammonia in alkaline media

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  3. Predictive modelling for contact angle of liquid metals and oxide ceramics by comparing Gaussian process regression with other machine learning methods

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  4. Correlation between microstructures and macroscopic properties of nickel/yttria-stabilized zirconia (Ni-YSZ) anodes: Meso-scale modeling and deep learning with convolutional neural networks

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  5. Novel Materials for Urban Farming

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  6. Performance of CO2 electrolysis using solid oxide electrolysis cell with Ni-YSZ as fuel electrode under different fuel atmospheres

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  7. High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications

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  8. Automated synthesis and data accumulation for fast production of high-performance Ni nanocatalysts

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  11. Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches

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  19. UTILIZAÇÃO DE TÉCNICAS DE CLASSIFICAÇÃO EM CONJUNTO DE DADOS SOBRE INCLUSÃO FINANCEIRA: UM ESTUDO BASEADO EM PAÍSES LATINOAMERICANOS

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  37. Machine-learning-assisted insight into the cathode catalyst layer in proton exchange membrane fuel cells

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  38. Advanced Cathode Materials for Protonic Ceramic Fuel Cells: Recent Progress and Future Perspectives

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  39. Prediction of the permeability of fibrous porous structures under the full flow regimes

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  40. Evaluation of Machine Learning Models on Electrochemical CO2 Reduction Using Human Curated Datasets

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  41. A Review of Performance Prediction Based on Machine Learning in Materials Science

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  42. Full-scale three-dimensional simulation of air-cooled proton exchange membrane fuel cell stack: Temperature spatial variation and comprehensive validation

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  43. Toward a Li‐Ion Battery Ontology Covering Production and Material Structure

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  44. Materials discovery of ion-selective membranes using artificial intelligence

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  45. Performance Characteristics of a Direct Ammonia Fuel Cell with an Anion Exchange Membrane

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  46. Machine Learning a Million Cycles as 2D Images from Practical Batteries for Electric Vehicle Applications

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  47. Machine learning accelerates the materials discovery

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  48. Toward a function realization of multi‐scale modeling for lithium‐ion battery based on CHAIN framework

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

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  50. Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges

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