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RESEARCH PAPER · ChemCatChem · 2021

Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts

Shinya Mine, Motoshi Takao, Taichi Yamaguchi, Takashi Toyao, Zen Maeno, S. M. A. Hakim Siddiki, Satoru Takakusagi, Ken‐ichi Shimizu, Ichigaku Takigawa

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Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel CatalystsOriginalConstructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts26Factors determining surface oxygen vacancy formation energy in ternary spinel structure oxides with zinc31Surface activation by electron scavenger metal nanorod adsorption on TiH 2 , TiC, TiN, and Ti 2 O 311Productivity Enhancement for the Oxidative Coupling of Methane in Adiabatic Layered-Bed Reactors5XGBoost‐based intelligence yield prediction and reaction factors analysis of amination reaction26Knowledge extraction in catalysis utilizing design of experiments and machine learning29High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La 2 O 3 -based catalysts for the oxidative coupling of methane16Machine learning for design principles for single atom catalysts towards electrochemical reactions116A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data12Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction toward Extrapolative Prediction of Novel Catalysts25Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence54Is it time to stop searching for better catalysts for oxidative coupling of methane?8Interpretable machine learning for knowledge generation in heterogeneous catalysis428Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter12A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys33Machine Learning: A New Paradigm in Computational Electrocatalysis113Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective6A deep neural network for oxidative coupling of methane trained on high-throughput experimental data4Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression33Indirect design of OCM catalysts through machine learning of catalyst surface oxygen species7Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane15Screening of steam-reforming catalysts using unsupervised machine learning10Bridging the complexity gap in computational heterogeneous catalysis with machine learning280Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use6Machine-learning-guided prediction of Cu-based electrocatalysts towards ethylene production in CO2 reduction18Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach61Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane69Divide to Conquer: On the Use of Distributed Feed Strategies in Oxidative Coupling of Methane2Delving into machine learning modeling of catalytic reactor system: a case study of steam methane reforming0Experimental discovery of novel ammonia synthesis catalysts via active learning21Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis60Machine learning assisted prediction of copper-based catalysts towards carbon dioxide electroreduction into carbon monoxide8Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation56Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane14A review of the application of Density Functional Theory and machine learning for oxidative coupling of methane reaction for ethylene production3From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190Toward accelerated discovery of solid catalysts using extrapolative machine learning approach10Data-driven Design of Catalytic Materials in Methane Oxidation Based on a Site Isolation Concept5Phosphate-containing alkali metal-doped TbOx/MgO catalysts for the oxidative coupling of methane2Accelerating catalytic experimentation of water gas shift reaction using machine learning models6Design of low temperature La 2 O 3 oxidative coupling of methane catalysts using feature engineering and automated sampling4Small-dataset-orientated data-driven screening for catalytic propane activation6A comparative analysis of machine learning predictive models for the oxidative coupling of methane reaction1Historical experimental data and theoretical volcano map-accelerated cross-scale design of a highly active and durable ternary alloy electrocatalyst for formic acid oxidation2Enhancing Catalyst Performance Prediction with Hybrid Quantum Neural Networks: A Comparative Study on Data Consistency Variation4New insights into the performance of biomass carbon-based supercapacitors based on interpretable machine learning approach14Aggregated Catalyst Physicochemical Descriptor-Driven Machine Learning for Catalyst Optimization: Insights into Oxidative-Coupling-of-Methane Dynamics and C2 Yields3A Review on Catalytic Ethanol Synthesis via Hydrogenation of Carbon Dioxide21From pages to patterns: Towards extracting catalytic knowledge from structure and text for transition-metal complexes and metal-organic frameworks6A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity5Catalyst Phylogenetic Tree: A Visualization Tool for Exploring Catalyst Research and Development from Tabular Data0Infrared Spectra‐Based Machine Learning Framework for Photocatalytic Reaction and Performance3Artificial Intelligence‐assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data1Design and Operation of Copper-Based Heterogeneous Catalysts for CO2 Conversion to Methanol Using Extreme Gradient Boosting4Development of Highly Active Catalysts for Low-Temperature CO 2 Hydrogenation to Methanol Using a Machine Learning Approach22Statistical and Predictive Analysis of Supported Catalytically Active Metal Solutions (SCALMS) in Propane Dehydrogenation2Extrapolative-Machine-Learning-Guided Discovery of Multielemental Heterogeneous Catalysts for Low-Temperature NO Reduction by H 211Chemical looping oxidative coupling of methane0Accelerating Discovery of CO2 Reduction Catalysts through Machine Learning–Guided Experiments0Machine Learning-Guided Design of Catalysts with SO 2 Resistance for Low-Temperature NH 3 –SCR Reaction7Thermo-Catalytic Carbon Dioxide Hydrogenation to Ethanol4Machine-Learning-Guided Discovery and Statistical Analysis of Multielemental Reverse Water–Gas Shift Catalysts1From Data to Discovery: Challenges and Solutions in AI-Driven Materials Science0Co-production of ethylene and hydrogen via oxidative coupling of methane: Improving techno-economics and sustainability under emerging hydrogen markets0Machine-Learning-Guided Discovery of CH 4 Combustion Catalysts Operating in the Presence of SO 22Potential, challenges and opportunities of machine learning model for plasma-based gas conversion: a critical review0Artificial Intelligence- and Machine Learning-Driven Strategies for Catalyst Design and Sustainable Chemical Processes5Machine learning accelerated development of CuZn-Based catalysts for syngas to higher alcohols: Virtual screening and experimental validation0Machine Learning-Assisted Development of High-Performance Ethanol Synthesis Catalysts via CO2 Hydrogenation0A Review on the Application of Machine Learning in Nitrogen Reduction Reaction0Explainable artificial intelligence shines a light on catalysis: methods, applications, and future directions0Reaction-Depth Learning Enables Apparent Yield Extrapolation in Catalyst Informatics0Descriptor‐Based Zero‐Shot Learning for Predicting Catalytic Constituent Elements0Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches0Engine exhaust waste heat recovery and utilization: synergistic optimization of Cu-based ternary catalysts for hydrogen-rich fuel production from green methanol cracking0

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  2. Factors determining surface oxygen vacancy formation energy in ternary spinel structure oxides with zinc

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  3. Surface activation by electron scavenger metal nanorod adsorption on TiH 2 , TiC, TiN, and Ti 2 O 3

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  4. Productivity Enhancement for the Oxidative Coupling of Methane in Adiabatic Layered-Bed Reactors

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  5. XGBoost‐based intelligence yield prediction and reaction factors analysis of amination reaction

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  10. Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction toward Extrapolative Prediction of Novel Catalysts

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  11. Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence

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  12. Is it time to stop searching for better catalysts for oxidative coupling of methane?

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  13. Interpretable machine learning for knowledge generation in heterogeneous catalysis

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  14. Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter

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  18. A deep neural network for oxidative coupling of methane trained on high-throughput experimental data

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  19. Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression

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