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RESEARCH PAPER · Catalysis Science & Technology · 2023

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

Shun Nishimura, Xinyue Li, Junya Ohyama, KEISUKE G. TAKAHASHI

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Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methaneOriginalModel-based catalyst screening and optimal experimental design for the oxidative coupling of methane3From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190Improved Catalytic Partial Oxidation of Methane via Lattice Oxygen Modification on Supported Copper Oxide Catalyst System5Phosphate-containing alkali metal-doped TbOx/MgO catalysts for the oxidative coupling of methane2Design of low temperature La 2 O 3 oxidative coupling of methane catalysts using feature engineering and automated sampling4ABO4 as an Active Catalyst Structure for Direct Partial CH4 Oxidation as Identified through Screening of Supported Catalysts5Digitalisation of catalytic processes for sustainable production of biobased chemicals and exploration of wider chemical space8Enhancement of Boehmite‐derived Aluminum Oxide Catalysis for Intramolecular Aldol Condensation of 2,5‐hexanedione to 3‐methyl‐2‐cyclopentenone by Lanthanum Loading and Thermal Treatment3Aggregated 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 Serendipity5Catalyst Phylogenetic Tree: A Visualization Tool for Exploring Catalyst Research and Development from Tabular Data0Compatibility Diagnosis for the Design of Oxidative Coupling of Methane Catalysts0Accelerate the discovery of OCM catalysts with machine-learning and machine-synthesis0Automated Feature Engineering and Model Aggregation for Data-Driven Oxidative Coupling of Methane Catalyst Design1Commercialization path of data-driven green catalytic technology: the application of machine learning in technology lifecycle management5

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15 direct citing papers saved.

  1. Model-based catalyst screening and optimal experimental design for the oxidative coupling of methane

    Digital Chemical Engineering · 2024-06-01

    Anjana Puliyanda

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  2. From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

    ACS Catalysis · 2024-07-24

    Jorge Benavides-Hernández, Franck Dumeignil

    Read original paper ↗ ·

  3. Improved Catalytic Partial Oxidation of Methane via Lattice Oxygen Modification on Supported Copper Oxide Catalyst System

    ChemCatChem · 2024-08-13

    Junya Ohyama, Hiroki Iwai, Daichi Takahashi, Masayuki Tsushida, Masato Machida, Shun Nishimura, KEISUKE G. TAKAHASHI

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  4. Phosphate-containing alkali metal-doped TbOx/MgO catalysts for the oxidative coupling of methane

    Applied Catalysis A General · 2024-08-22

    Andrew S. Jones, Nicolas Alfonso Vargas, Helena E. Hagelin‐Weaver

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  5. Design of low temperature La 2 O 3 oxidative coupling of methane catalysts using feature engineering and automated sampling

    Catalysis Science & Technology · 2024-11-25

    Fernando Garcia-Escobar, Lauren Takahashi, Ali Shaaban, Shun Nishimura, KEISUKE G. TAKAHASHI

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  6. ABO4 as an Active Catalyst Structure for Direct Partial CH4 Oxidation as Identified through Screening of Supported Catalysts

    ACS Catalysis · 2024-12-24

    Junya Ohyama, Yuriko Yoshioka, Momoka Tsukamoto, Rina Kuroki, Daichi Takahashi, Keisuke Awaya, Masato Machida, Kotaro Higashi, Tomoya Uruga, Naomi Kawamura, Shun Nishimura, KEISUKE G. TAKAHASHI

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  7. Digitalisation of catalytic processes for sustainable production of biobased chemicals and exploration of wider chemical space

    Catalysis Science & Technology · 2025-01-01

    Firdaus Parveen, Anna Grace Slater

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  8. Enhancement of Boehmite‐derived Aluminum Oxide Catalysis for Intramolecular Aldol Condensation of 2,5‐hexanedione to 3‐methyl‐2‐cyclopentenone by Lanthanum Loading and Thermal Treatment

    ChemistrySelect · 2025-02-01

    Shun Nishimura, Zijian Ling

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  9. Aggregated Catalyst Physicochemical Descriptor-Driven Machine Learning for Catalyst Optimization: Insights into Oxidative-Coupling-of-Methane Dynamics and C2 Yields

    Catalysts · 2025-04-13

    Mohamed M. Ezz, Ayman Mohamed Mostafa, Alaa Alaerjan, Hisham Allahem, Bader Aldughayfiq, Hassan M.A. Hassan, Rasha M.K. Mohamed

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  10. A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity

    ACS Catalysis · 2025-05-08

    Sunao Nakanowatari, KEISUKE G. TAKAHASHI, Hieu‐Chi Dam, Toshiaki Taniike

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  11. Catalyst Phylogenetic Tree: A Visualization Tool for Exploring Catalyst Research and Development from Tabular Data

    The Journal of Physical Chemistry Letters · 2025-06-16

    Sunao Nakanowatari, Toshiaki Taniike

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  12. Compatibility Diagnosis for the Design of Oxidative Coupling of Methane Catalysts

    The Journal of Physical Chemistry Letters · 2025-11-15

    Kei Kobayashi, Fernando Garcia‐Escobar, Yoshiki Hasukawa, Rensuke Koiwai, Lauren Takahashi, KEISUKE G. TAKAHASHI

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  13. Accelerate the discovery of OCM catalysts with machine-learning and machine-synthesis

    Discover Catalysis. · 2025-12-08

    Jifeng Ouyang, Yutao Ren, Shihui Zou, Xutao Chen, Jie Fan

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  14. Automated Feature Engineering and Model Aggregation for Data-Driven Oxidative Coupling of Methane Catalyst Design

    ACS Applied Materials & Interfaces · 2025-12-25

    Fernando Garcia‐Escobar, Aya Fujiwara, Toshiaki Taniike, KEISUKE G. TAKAHASHI

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  15. Commercialization path of data-driven green catalytic technology: the application of machine learning in technology lifecycle management

    Environmental Research · 2026-02-08

    Tingfa Zhou, Chao Hu

    Read original paper ↗ ·
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