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RESEARCH PAPER · Advanced Functional Materials · 2022

Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Jianwen Liu, Wenzhi Luo, Lei Wang, Jiujun Zhang, Xian‐Zhu Fu, Jing‐Li Luo

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Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine LearningOriginalMulti-atom cluster catalysts for efficient electrocatalysis256N-doped graphene for electrocatalytic O 2 and CO 2 reduction37Advanced Materials for Thin‐Film Solid Oxide Fuel Cells: Recent Progress and Challenges in Boosting the Device Performance at Low Temperatures232Controllable Lattice Expansion of Monodisperse Face-Centered Cubic Pd–Ag Nanoparticles for C1 and C2 Alcohol Oxidation: The Role of Core–Sheath Lattice Mismatch30Catalytic effect in Li-S batteries: From band theory to practical application169Computation and Machine Learning for Catalyst Discovery2Interfacial component coupling effects towards precise heterostructure design for efficient electrocatalytic water splitting158Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane51Research Progress in High-Throughput Screening of CO2 Reduction Catalysts31Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects24Designing catalysts via evolutionary-based optimization techniques18Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives113Breaking the Volcano-Shaped Relationship for Highly Efficient Electrocatalytic Nitrogen Reduction: A Computational Guideline30Time-reversal equivariant neural network potential and Hamiltonian for magnetic materials5Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys43Data-driven design of electrocatalysts: principle, progress, and perspective46First-principles calculation method for periodic system under external electromagnetic field2High-entropy alloys in electrocatalysis: from fundamentals to applications540Hydrogen society: from present to future524Retrospective insights into recent MXene-based catalysts for CO 2 electro/photoreduction: how far have we gone?45Tailoring supports for enhancing the electrocatalytic hydrogen evolution performance of platinum species: a review96The rational design of high-performance graphene-based single-atom electrocatalysts for the ORR using machine learning10A corpus of CO2 electrocatalytic reduction process extracted from the scientific literature18Recent advances in developing multiscale descriptor approach for the design of oxygen redox electrocatalysts23Addressing complexity in catalyst design: From volcanos and scaling to more sophisticated design strategies77The importance of a charge transfer descriptor for screening potential CO2 reduction electrocatalysts114Revisiting the Role of Discharge Products in Li–CO 2 Batteries132Machine Learning Descriptors for Data‐Driven Catalysis Study154X-ray photoelectron spectroscopy meets electrochemistry: From UHV to operando conditions1Design high-entropy electrocatalyst via interpretable deep graph attention learning73First‐Row Transition Metals for Catalyzing Oxygen Redox51Electrochemical Valorization of Glycerol via Electrocatalytic Reduction into Biofuels: A Review30Machine Learning Accelerated Discovery of Subnanoparticles for Electrocatalytic Hydrogen Evolution1Recent advances in the data-driven development of emerging electrocatalysts16Engineering the spin configuration of electrocatalysts for electrochemical renewable conversions28A Review of Transition Metal Boride, Carbide, Pnictide, and Chalcogenide Water Oxidation Electrocatalysts295Machine-Learning assisted screening of double metal catalysts for CO2 electroreduction to CH429Designing bifunctional catalysts for urea electrolysis: progress and perspectives54Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective76Computational Assistance in the Design of Efficient Oxygen Reduction Reaction Electrocatalysts1Retracted: Uncovering the Link Between Deep Learning and Data Science Practices0Spectroscopy-Guided Deep Learning Predicts Solid–Liquid Surface Adsorbate Properties in Unseen Solvents10A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods25Advancing electrocatalytic reactions through mapping key intermediates to active sites via descriptors103AI and ML for selecting viable electrocatalysts: progress and perspectives51Approaches to Construct High-Performance Mg–Air Batteries0Automation and machine learning augmented by large language models in a catalysis study107Rational design of water splitting electrocatalysts through computational insights21Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis60Allying interfacial engineering of 2D carbon nanosheet‐, graphene‐, and graphdiyne‐based heterostructured electrocatalysts toward hydrogen evolution and overall water splitting39Progress in Single/Multi Atoms and 2D‐Nanomaterials for Electro/Photocatalytic Nitrogen Reduction: Experimental, Computational and Machine Leaning Developments88Data‐Driven Screening of Pivotal Subunits in Edge‐Anchored Single Atom Catalysts for Oxygen Reactions46The emerging Sr2FeMoO6-based electrocatalysts for solid oxide electrochemical cell: synthesis, modulation and applications10Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys17Machine learning for CO 2 capture and conversion: A review60Additive Manufacturing: A Paradigm Shift in Revolutionizing Catalysis with 3D Printed Photocatalysts and Electrocatalysts Toward Environmental Sustainability36A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis27Work Function‐Guided Electrocatalyst Design213Electrochemical energy conversion and storage processes with machine learning18Machine‐Learning‐Assisted Discovery of Mechanosynthesized Lead‐Free Metal Halide Perovskites for the Oxidative Photocatalytic Cleavage of Alkenes22Critical Review and Analytical Energetic Assessment of Key-parameters for Industry 4.0 Development in Italy through a Cross-comparison with the EU Digitalization Scenarios1Recent Advances on Carbon‐Based Metal‐Free Electrocatalysts for Energy and Chemical Conversions136Enhancing performance of lower-temperature solid oxide fuel cell cathodes through surface engineering69Machine Learning‐Assisted Design of Nitrogen‐Rich Covalent Triazine Frameworks Photocatalysts16Recent progress in high‐entropy nanocatalysts23Ni-based electrocatalysts for urea oxidation reaction: Mechanistic insights and recent advancements33Screening A-site ordered quadruple perovskites for alkaline hydrogen evolution reaction via unifying electronic configuration descriptor7Bridging gaps between lab- and fab-oriented anode design for proton exchange membrane water electrolyzers51Design of Electrocatalysts with High Performance Based on Thermodynamics and Kinetics: Progress and Prospects35Edge-doped substituents as an emerging atomic-level strategy for enhancing M–N 4 –C single-atom catalysts in electrocatalysis of the ORR, OER, and HER29Interference mechanism of electrolyte cations on vanadium-oxygen binary doped carbon nitride for hydrogen evolution from artificial seawater splitting: Coupling experiments, DFT calculations and machine learning17The use of single-metal atom-based photocatalysts for the production of ammonia through photocatalytic nitrogen fixation23Surface Reconstruction Induced by Preconditioning in Different Electrolytes Impacts Electrooxidation of Solketal on Multi‐Metal‐Based Catalysts5Machine learning-driven breakthroughs in water electrolysis and supercapacitors17Porous organic material-based atomically dispersed metal electrocatalysts25Rapid synthesis of metastable materials for electrocatalysis46Atomistic simulations of heterogeneous electrocatalysis at the center of sustainable carbon feedstocks2Catalysis‐Driven Sulfur Conversion: From Electrolyte‐Flooded to Solid‐State Batteries19Graphdiyne-based metal-free catalysts: Innovations in synthesis, properties, functionalization, morphology and applications32Machine learning assisted Co3O4/NiO popsicle sticks-infused electrospun nanofibers for efficient oxygen evolution reaction9Guided electrocatalyst design through in-situ techniques and data mining approaches8Electrochemical reduction of CO2 to liquid products: Factors influencing production and selectivity19Machine learning-driven prediction and optimization of selective glycerol electrocatalytic reduction into propanediols10Data-driven design of metal oxide electrocatalysts with low tafel slope in oxygen evolution reaction12Nano high-entropy materials for bifunctional oxygen electrocatalysis: Rational design, structure-activity relationships, and applications in rechargeable metal-air batteries15Rational design of catalysts for lithium–sulfur batteries based on descriptors: progress and prospects12Strategies for Urban Hydrogen Utilization: A Focus on Fuel Cell Technology and Hydrogen Infrastructure Development2The crystallographic active sites analysis of Lumbricus rubellus worm derived protein-based bio-organic electrocatalyst0GAN-driven discovery of low-cost non-noble metallic electrocatalysts for glycerol electroreduction3Electrodeposition of multi component alloys for hydrogen evolution reaction – A review3Recent advances in tailored nanostructured ozonation catalysts for enhanced VOCs removal: synergistic optimization of scale configuration and electronic microenvironment15Electrosynthesis of Urea from Carbon Dioxide and Waste Nitrates: History, Recent Progress, and Future Prospects24Opportunities and perspectives of artificial intelligence in electrocatalysts design for water electrolysis13Spin Matters: A Multidisciplinary Roadmap to Understanding Spin Effects in Oxygen Evolution Reaction During Water Electrolysis13AI-Accelerated Discovery of Electrocatalyst Materials26Few-shot learning breaks the data bottlenecks in perovskite materials design2Machine Learning-Assisted DFT Screening of Nitrogen-Doped Graphene Diatomic Catalysts for Nitrogen Reduction Reaction6Prediction of the elastic properties and electrical resistance of halide glass based on interpretable machine learning1Descriptors of Electrocatalysis for CO2 Reduction to C2+ Products Formation10Deciphering E g Occupancy Descriptor in Oxygen Electrocatalysis11Deciphering E g Occupancy Descriptor in Oxygen Electrocatalysis1Exploring the Influence of Electronic Structure on Electrocatalytic Nitrate Reduction to Ammonia2Rationalizing the d-Band Model from Theory to Practice in Catalyst Design113Data-driven screening of trimetallic electrocatalysts for enhanced water splitting activity2Challenges in Scaling Up 2D Material-Based Electrocatalysts2Engineering the dynamic reactant–electrode interface in electroreduction systems: advances, challenges and prospects0Machine learning-driven high-throughput screening of electrocatalysts and electrolytes for electrochemical surfaces and interfaces5Machine learning-guided design principles for single-atom catalysts in persulfate-based advanced oxidation processes1Modulating the electronic properties and hydrogen evolution reaction performance of precious-metal on 2D-SiC via surface defects0Molecular catalysts for the oxygen reduction reaction based on earth abundant transition metals: progress, limitations, and future opportunities4Accelerated discovery of MM ′ XT 2 MXenes for catalysis, electronics, and energy storage using supervised machine learning1Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability6Catalytic Ozonation of Volatile Organic Compounds: Reaction Mechanisms, Catalyst Design, and Characterization2Design, characterization, and dynamic behavior of atomically dispersed Pt catalysts for water splitting and oxygen reduction: From experimental insights to machine-learning-assisted analysis3Precisely tuned CuFe-based multimetallic catalysts: machine learning guided DFT design for ultra-efficient overall water splitting0Accelerating single-atom ORR catalyst discovery through theory and machine learning: a critical review13Potential, challenges and opportunities of machine learning model for plasma-based gas conversion: a critical review0Data-driven design of electrocatalysts: Machine learning workflows, paradigm shifts, and applications4Machine Learning for Superconductor Discovery: From Data-Driven Insights to Accelerated Design1Combining DFT calculations and machine learning for rational design of hydrogen electrocatalysts2AI-guided surface and defect engineering of sustainable carbon electrocatalysts: Descriptor discovery, active-site validation, and scalable electrochemical conversion2Machine Learning-Guided Prediction of Low-Barrier N 2 and CO 2 Activation for Catalyst Design0Learning crystal–descriptor synergy: A multimodal deep learning approach for band gap prediction in chalcogenide materials0From Data to Discovery: Crafting Data‐Driven Frameworks for Advancing OER Electrocatalysts0How can high-throughput experimentation accelerate the rational design of electro-catalysts for sustainable energy conversion?0Artificial Intelligence and Machine Learning for Oxygen Reduction Reaction: A Review on Catalyst Discovery and Durability Modeling0Extraordinary Thermal Radiation Suppression Enables High‐Entropy Oxide Ceramics for Ultrahigh‐Temperature Protection up to 2000 K0

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