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RESEARCH PAPER · ACS Catalysis · 2021

Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane

Thanh Nhat Nguyen, Sunao Nakanowatari, Thuy Phuong Nhat Tran, Ashutosh Thakur, Lauren Takahashi, KEISUKE G. TAKAHASHI, Toshiaki Taniike

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Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of MethaneOriginalConstructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts26Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem8Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane23Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts12Factors to influence low-temperature performance of supported Mn–Na2WO4 in oxidative coupling of methane11Knowledge 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 reactions116The Rise of Catalysts Informatics1A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data12Is it time to stop searching for better catalysts for oxidative coupling of methane?8Oxidative coupling of methane in chemical looping design32Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter12Electrochemical CO 2 Reduction Catalyzed by Copper Molecular Complexes: The Influence of Ligand Structure56Designing transformer oil immersion cooling servers for machine learning and first principle calculations3Data in Materials and Catalysts Informatics4Biomacromolecule-Assisted Screening for Reaction Discovery and Catalyst Optimization15Data-Driven Inference of Synthesis Guidelines for High-Performance Zeolite-Based Selective Catalytic Reduction Catalysts at Low Temperatures21Why big data and compute are not necessarily the path to big materials science51Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane51A deep neural network for oxidative coupling of methane trained on high-throughput experimental data4Catalysts informatics: paradigm shift towards data-driven catalyst design61Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane15What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO 2 reduction? A data-driven retrospective4The value of negative results in data-driven catalysis research72Addressing complexity in catalyst design: From volcanos and scaling to more sophisticated design strategies77High-throughput screening of multimetallic catalysts for three-way catalysis1Exploration of Ethanol-to-Butadiene Catalysts by High-Throughput Experimentation and Machine Learning22Machine Learning Descriptors for Data‐Driven Catalysis Study154High-throughput screening of multimetallic catalysts for three-way catalysis3Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis60MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models6Model-based catalyst screening and optimal experimental design for the oxidative coupling of methane3Data-driven Design of Catalytic Materials in Methane Oxidation Based on a Site Isolation Concept5Catalyst breakthroughs in methane dry reforming: Employing machine learning for future advancements15The influence of the catalyst preparation methods on performance of Mn/Na2WO4/SiO2 in oxidative coupling of methane5Design of low temperature La 2 O 3 oxidative coupling of methane catalysts using feature engineering and automated sampling4A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery37Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts1Unbiased dataset for methane dry reforming and catalyst design guidelines obtained by high-throughput experimentation and machine learning12Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering6Clc-db: an open-source online database of chiral ligands and catalysts6Aggregated Catalyst Physicochemical Descriptor-Driven Machine Learning for Catalyst Optimization: Insights into Oxidative-Coupling-of-Methane Dynamics and C2 Yields3A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity5Machine learning perspective: Revealing deep mechanisms and new advances in adsorption and catalysis of gaseous molecules18Catalyst Phylogenetic Tree: A Visualization Tool for Exploring Catalyst Research and Development from Tabular Data0Artificial Intelligence‐assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data1Compatibility Diagnosis for the Design of Oxidative Coupling of Methane Catalysts0Automated Feature Engineering and Model Aggregation for Data-Driven Oxidative Coupling of Methane Catalyst Design1Designing less noble yet high-performing multimetallic catalysts for catalytic converters using a high-throughput approach0Fast and accurate explanations of distance-based classifiers by uncovering latent explanatory structures1Heterogeneous catalysts for the direct and oxidative dehydrogenation of n-butane to butadiene: A comprehensive review covering thermodynamics, catalyst development, kinetics and catalyst informatics0Catalyst and Catalysis Co-Exploration in Methane Utilization0High-Throughput Experimentation Platform for Hydroxide Electrodeposition and Electrochemical Evaluation of Electrocatalysts0

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  1. Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

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  2. Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

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  3. Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of Methane

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  4. Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts

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  5. Factors to influence low-temperature performance of supported Mn–Na2WO4 in oxidative coupling of methane

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    Thanh Nhat Nguyen, Kalaivani Seenivasan, Sunao Nakanowatari, Priyank Mohan, Thuy Phuong Nhat Tran, Shun Nishimura, KEISUKE G. TAKAHASHI, Toshiaki Taniike

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  6. Knowledge extraction in catalysis utilizing design of experiments and machine learning

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  7. High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La 2 O 3 -based catalysts for the oxidative coupling of methane

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  8. Machine learning for design principles for single atom catalysts towards electrochemical reactions

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  9. The Rise of Catalysts Informatics

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  10. A chemistry‐inspired neural network kinetic model for oxidative coupling of methane from high‐throughput data

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

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  12. Oxidative coupling of methane in chemical looping design

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

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  14. Electrochemical CO 2 Reduction Catalyzed by Copper Molecular Complexes: The Influence of Ligand Structure

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  15. Designing transformer oil immersion cooling servers for machine learning and first principle calculations

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  16. Data in Materials and Catalysts Informatics

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  17. Biomacromolecule-Assisted Screening for Reaction Discovery and Catalyst Optimization

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  18. Data-Driven Inference of Synthesis Guidelines for High-Performance Zeolite-Based Selective Catalytic Reduction Catalysts at Low Temperatures

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  19. Why big data and compute are not necessarily the path to big materials science

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  20. Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane

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

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  23. Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane

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