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RESEARCH PAPER · Journal of Materials Chemistry A · 2020

Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning

Shiru Lin, Haoxiang Xu, Yekun Wang, Xiao Cheng Zeng, Zhongfang Chen

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Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learningOriginalTheoretical investigation on catalytic mechanisms of oxygen reduction and carbon monoxide oxidation on the MnN x system12Transition metal–N 4 embedded black phosphorus carbide as a high-performance bifunctional electrocatalyst for ORR/OER55Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half‐Metals/Metals146All Boron Atoms in a ScB12 Monolayer Contribute to the Hydrogen Evolution Reaction21Theoretical Understandings of Graphene-based Metal Single-Atom Catalysts: Stability and Catalytic Performance711Coordination Engineering of Single‐Atom Catalysts for the Oxygen Reduction Reaction: A Review456Design of Single Atom Catalysts20Multimetallic nanostructures for electrocatalytic oxygen evolution reaction in acidic media28Recent Progress on Two-Dimensional Materials472Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C 2 N by DFT and machine learning191Energy parameter and electronic descriptor for carbon based catalyst predicted using QM/ML40Rh@C8N8 monolayer as a promising single-atom-catalyst for overall water splitting41High-Throughput Screening of a Single-Atom Alloy for Electroreduction of Dinitrogen to Ammonia91Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts105Accelerating Optimizing the Design of Carbon‐based Electrocatalyst via Machine Learning21Engineering the Local Coordination Environment of Single-Atom Catalysts and Their Applications in Photocatalytic Water Splitting: A Review58Encoding the atomic structure for machine learning in materials science85Computation of the Thermal Expansion Coefficient of Graphene with Gaussian Approximation Potentials15Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale166Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale56Noble-Metal-Free Multicomponent Nanointegration for Sustainable Energy Conversion264Linear and nonlinear machine learning correlation of transition metal cluster characteristics3Recent Progress on Structurally Ordered Materials for Electrocatalysis112CO2 Adsorptions on d-Block-Metal-Doped Nickel Nanoparticles: Unexpected Adsorption Configurations Predicted by Machine Intelligence5Machine Learning in Screening High Performance Electrocatalysts for CO2 Reduction110Accelerated Discovery of Single‐Atom Catalysts for Nitrogen Fixation via Machine Learning80Data-driven design and controllable synthesis of Pt/carbon electrocatalysts for H2 evolution18Theory-oriented screening and discovery of advanced energy transformation materials in electrocatalysis406Targeted design of advanced electrocatalysts by machine learning110Discovery of Lead‐Free Perovskites for High‐Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities83A theoretical roadmap for the best oxygen reduction activity in two-dimensional transition metal tellurides8Gradient Boosting Algorithm for Current-Voltage Prediction of Fuel Cells8Machine learning for design principles for single atom catalysts towards electrochemical reactions116Machine Learning Utilized for the Development of Proton Exchange Membrane Electrolyzers51Revealing the Oxygen Reduction/Evolution Reaction Activity Origin of Carbon-Nitride-Related Single-Atom Catalysts: Quantum Chemistry in Artificial Intelligence90Theoretically revealing the activity origin of the hydrogen evolution reaction on carbon-based single-atom catalysts and finding ideal catalysts for water splitting37Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning127Establishing the Principal Descriptor for Electrochemical Urea Production via the Dispersed Dual‐Metals Anchored on the N‐Decorated Graphene92Modulating the Local Coordination Environment of Single‐Atom Catalysts for Enhanced Catalytic Performance in Hydrogen/Oxygen Evolution Reaction129Introduction to Supported Metal Single Atom Catalysis2A first-principles study on the electrochemical reaction activity of 3d transition metal single-atom catalysts in nitrogen-doped graphene: Trends and hints125Deep dive into machine learning density functional theory for materials science and chemistry111An accurate and general local electronic structural descriptor for oxygen reduction on metal surface alloys: Potential of breaking scaling relationship and application to PtZn ones with superior activity11Ligands dependent electrocatalytic nitrogen reduction performance in d-π conjugated molecules16Experimental and Theoretical Advances on Single Atom and Atomic Cluster‐Decorated Low‐Dimensional Platforms towards Superior Electrocatalysts68Recent progress towards a universal machine learning model for reaction energetics in heterogeneous catalysis20Universal Principles for the Rational Design of Single Atom Electrocatalysts? Handle with Care155Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction115Application of Machine Learning in Optimizing Proton Exchange Membrane Fuel Cells: A Review171Understanding the structure-performance relationship of active sites at atomic scale537Theoretical inspection of TM-P4C single-atom electrocatalysts: High performance for oxygen reduction and evolution reactions12Transition-Metal Interlink Neural Network: Machine Learning of 2D Metal–Organic Frameworks with High Magnetic Anisotropy21Machine-learning-assisted insight into the cathode catalyst layer in proton exchange membrane fuel cells42Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Tailoring Bond Microenvironments and Reaction Pathways of Single‐Atom Catalysts for Efficient Water Electrolysis7Tailoring Bond Microenvironments and Reaction Pathways of Single‐Atom Catalysts for Efficient Water Electrolysis77Utilizing High Coordination Diversity in Carbon Nanocone Supported Catalytic Single-Atom Sites for Screening of Optimal Activity9Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects41Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction1Theory, properties and engineering of 2D magnetic materials85CdN4C0-gra as efficient trifunctional electrocatalyst for the HER, OER and ORR: A density functional theory study68Tuning OER Electrocatalysts toward LOM Pathway through the Lens of Multi-Descriptor Feature Selection by Artificial Intelligence-Based Approach51Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction0Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning12Identifying key factors of peroxymonosulfate activation on single-atom M–N–C catalysts: a combined density functional theory and machine learning study50Machine learning enabled rational design of atomic catalysts for electrochemical reactions19Paradigm in single-atom electrocatalysts for dinitrogen reduction to ammonia3The Defect Chemistry Study of Single Transition-Metal Atoms Anchored on Ptse2 Monolayer for the Rational Design of Bifunctional Oer/Orr Electrocatalysts0The rational design of high-performance graphene-based single-atom electrocatalysts for the ORR using machine learning10Curvature effect on graphene-based Co/Ni single-atom catalysts74Rational Design of Atomically Dispersed Metal Site Electrocatalysts for Oxygen Reduction Reaction59Tuning the Coordination Environment of Carbon‐Based Single‐Atom Catalysts via Doping with Multiple Heteroatoms and Their Applications in Electrocatalysis229High‐Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor78High‐Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor19Aramid-based highly conductive composite films by incorporating graphene for electromagnetic interference shielding and Joule heating applications44High throughput screening of single atomic catalysts with optimized local structures for the electrochemical oxygen reduction by machine learning56Building up a general selection strategy and catalytic performance prediction expressions of heteronuclear double-atom catalysts for N2 reduction69Recent advances in developing multiscale descriptor approach for the design of oxygen redox electrocatalysts23Review of Carbon Support Coordination Environments for Single Metal Atom Electrocatalysts (SACS)373Prediction of sintered density of binary W(Mo) alloys using machine learning27M-N 4 -Gr/MXene Heterojunction Nanosheets as Oxygen Reduction and Evolution Reaction Catalysts: Machine Learning and Density Functional Theory Insights52Optimization of cathode catalyst layer of membrane electrode assembly for polymer electrolyte fuel cells using machine learning26Machine learning screening of high-performance single-atom electrocatalysts for two-electron oxygen reduction reaction46High-Throughput Screening of Gas Sensor Materials for Decomposition Products of Eco-Friendly Insulation Medium by Machine Learning65Machine Learning Descriptors for Data‐Driven Catalysis Study154A Surrogate Machine Learning Model for the Design of Single-Atom Catalyst on Carbon and Porphyrin Supports towards Electrochemistry56Non-noble metal single atom catalysts for electrochemical energy conversion reactions45Experimental and numerical analysis of a catalyst layer in the membrane electrode assembly of polymer electrolyte membrane fuel cells4First‐Row Transition Metals for Catalyzing Oxygen Redox51Metal-Doped C 3 B Monolayer as the Promising Electrocatalyst for Hydrogen/Oxygen Evolution Reaction: A Combined Density Functional Theory and Machine Learning Study37Theoretical investigation on the bifunctional hydrogen/oxygen electrode catalysis in Cd–N–C-doped graphene systems regulated by the active center configuration21Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review10Catalytic reactivity descriptors of metal‐nitrogen‐doped carbon catalysts for electrocatalysis54Transforming main-group elements into highly active single-atom electrocatalysts for oxygen reduction reactions7The importance of surface coverages in the rational design of electrocatalysts24From prediction to design: Recent advances in machine learning for the study of 2D materials90Machine-Learning-Accelerated Screening of Double-Atom/Cluster Electrocatalysts for the Oxygen Reduction Reaction27MatGPT: A Vane of Materials Informatics from Past, Present, to Future74Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction207Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing22Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing1Mechanistic understanding on effect of doping nitrogen with graphene supported single-atom Ir toward HER and OER: A computational consideration19Hybrid Double Atom Catalysts for Hydrogen Evolution Reaction: A Sweet Marriage of Metal and Nonmetal79Identifying Molecular Structure–Energy Level Quantitative Relationship of Thermally Activated Delayed Fluorescence Materials Using Machine Learning11Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective76Unraveling the effect of single atom catalysts on the charging behavior of nonaqueous Mg–CO 2 batteries: a combined density functional theory and machine learning approach20Theoretical analysis of single Zn atoms with N/C on graphene promoting oxygen redox in alkaline medium8A Comparative Review: Research in Safety and Sustainability of Carbon Nanomaterials Without and With Machine Learning Assistance11Machine learning-enabled fast exploration of stable and active single-atom catalysts for oxygen evolution reaction21Rational design of single transition-metal atoms anchored on a PtSe 2 monolayer as bifunctional OER/ORR electrocatalysts: a defect chemistry and machine learning study62Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation239The atomic structural descriptor based on cluster expansion method and superior performance of oxygen reduction on PtTi surface alloys from theoretical perspective5Machine learning accelerates the screening of single-atom catalysts towards CO2 electroreduction30Computational screening of transition metal anchored C9N10 as the highly active and selective electrocatalysts for NH3 synthesis11Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys17Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction25Recent Advancements in Scalable Hydrogen Generation: An Integrated Approach of Experiments, Computation, and Machine Learning1Coordination environment manipulation of single atom catalysts: Regulation strategies, characterization techniques and applications65Multi‐Task Mixture Density Graph Neural Networks for Predicting Catalyst Performance13Directly predicting N2 electroreduction reaction free energy using interpretable machine learning with non-DFT calculated features29A review of advancements in theoretical simulation of oxygen reduction reaction and oxygen evolution reaction single-atom catalysts10Rational design of Pt-anchored single-atom alloy electrocatalysts for NO-to-NH3 conversion by density functional theory and machine learning24An application of graph neural network in pollution abatement: Acceleration heterogeneous catalyst design1Catalysis in the digital age: Unlocking the power of data with machine learning75Machine learning applications on proton exchange membrane water electrolyzers: A component-level overview14Application of machine learning for material prediction and design in the environmental remediation15Theoretical insight on the delicate regulation of coordination environments for carbon-nitrogen-supported single-atom metals for OER and ORR2Redox photocatalysis CuFe-BTC for nitrate reduction and formaldehyde oxidation11DFT calculations and machine learning for the study of ethane dehydrogenation on the heteroatom-doped graphene supported Pt SACs5Machine learning models for easily obtainable descriptors of the electrocatalytic properties of Ag–Pd–Ir nanoalloys toward the formate oxidation reaction9Machine learning-driven breakthroughs in water electrolysis and supercapacitors17Sustainable carbon materials in environmental and energy applications47Theoretical exploration of a single-atom catalyst anchored on β 12 -borophene for electrochemical nitrate reduction: catalyst screening and mechanistic insight5Unraveling active ensembles consisting of clusters and single atoms for oxygen reduction: a synergy of machine learning and DFT calculations8Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion28Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction33Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development13Does Doping Always Increase Activity? Theoretical Insights into Non-metallic Doping Engineering of Corrugated Graphene0Machine learning assisted screening of non-metal doped MXenes catalysts for hydrogen evolution reaction24The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design28Auxetic γ-SnO monolayer as a high-performance catalyst for light-driven water splitting and oxygen reduction: Unveiling screening descriptors via machine learning-assisted dimensionality reduction strategy9Machine-learning-assisted design of cathode catalysts for metal-sulfur/oxygen/carbon dioxide batteries8Machine learning-assisted screening of transition metal-doped TMDs for binding energy and charge transfer prediction2A machine learning model with minimize feature parameters for multi-type hydrogen evolution catalyst prediction41Big data-driven machine learning transformation for atomic-scale heterogeneous catalyst design: a critical review11Machine Learning-Assisted Design of Transition-Metal-Doped Cobalt Phosphide Electrocatalysts for Hydrogen Evolution10Electrocatalysts for the Oxygen Reduction Reaction in Proton Exchange Membrane Fuel Cells: Significant Advances, Major Challenges, and Future Directions64Machine learning-assisted prediction and optimization of electrocatalytic activity16Feature-Extended Descriptor Construction for Prediction of Consecutive Elementary Reaction Energies in Methane Oxidation1Machine Learning on a Synergistic Transition Metal Dual‐Atom Surface for Efficient Decomposition of Ammonia8

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  4. All Boron Atoms in a ScB12 Monolayer Contribute to the Hydrogen Evolution Reaction

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