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RESEARCH PAPER · ChemCatChem · 2021

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

Sunao Nakanowatari, Thanh Nhat Nguyen, Hiroki Chikuma, Aya Fujiwara, Kalaivani Seenivasan, Ashutosh Thakur, Lauren Takahashi, KEISUKE G. TAKAHASHI, Toshiaki Taniike

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42 direct citing works

Extraction of Catalyst Design Heuristics from Random Catalyst Dataset and their Utilization in Catalyst Development for Oxidative Coupling of MethaneOriginalConstructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts26Factors 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 reactions116Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter12Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane51High-Throughput Experimentation for Resource-Efficient Discovery of Methane Functionalization Catalysts1A deep neural network for oxidative coupling of methane trained on high-throughput experimental data4Catalysts informatics: paradigm shift towards data-driven catalyst design61High-throughput screening of multimetallic catalysts for three-way catalysis1Machine Learning Descriptors for Data‐Driven Catalysis Study154Fine-tuning channel structure and surface chemistry of stable bismuth-organic frameworks for efficient C2H4 purification through reversely trapping CO2 and C2H221High-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 Models6Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts1Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering6Aggregated Catalyst Physicochemical Descriptor-Driven Machine Learning for Catalyst Optimization: Insights into Oxidative-Coupling-of-Methane Dynamics and C2 Yields3Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation4A Data-Science Approach to Experimental Catalyst Discovery: Integrating Exploration, Exploitation, and Serendipity5Automated 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 approach0

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23 direct citing papers saved.

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

    Chemical Science · 2021-01-01

    Lauren Takahashi, Thanh Nhat Nguyen, Sunao Nakanowatari, Aya Fujiwara, Toshiaki Taniike, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

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

    Molecular Catalysis · 2021-11-01

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

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

    Current Opinion in Chemical Engineering · 2021-12-29

    Blake MacQueen, Rasika Jayarathna, Jochen A. Lauterbach

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  4. 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

    Catalysis Science & Technology · 2022-01-01

    Shun Nishimura, Son Dinh Le, Itsuki Miyazato, Jun Fujima, Toshiaki Taniike, Junya Ohyama, KEISUKE G. TAKAHASHI

    Read original paper ↗ ·

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

    Journal of Materials Chemistry A · 2022-01-01

    Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain, Patrick Ryan Galligan, Hoilun Wong, Zhenjing Liu, Hongwei Liu, Yuting Cai, William A. Goddard, Zhengtang Luo

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

    Industrial & Engineering Chemistry Research · 2022-04-08

    Shun Nishimura, Junya Ohyama, Xinyue Li, Itsuki Miyazato, Toshiaki Taniike, KEISUKE G. TAKAHASHI

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

    ACS Catalysis · 2022-09-08

    Sora Ishioka, Aya Fujiwara, Sunao Nakanowatari, Lauren Takahashi, Toshiaki Taniike, KEISUKE G. TAKAHASHI

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  8. High-Throughput Experimentation for Resource-Efficient Discovery of Methane Functionalization Catalysts

    ACS symposium series · 2022-11-15

    Orion Staples, Jacob S. Mohar, Daniel J. Mindiola

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

    Journal of Physics Energy · 2022-11-30

    Klea Ziu, Rubén Solozabal, Srinivas Rangarajan, Martin Takáč

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  10. Catalysts informatics: paradigm shift towards data-driven catalyst design

    Chemical Communications · 2023-01-01

    KEISUKE G. TAKAHASHI, Junya Ohyama, Shun Nishimura, Jun Fujima, Lauren Takahashi, Takeaki Uno, Toshiaki Taniike

    Read original paper ↗ ·

  11. High-throughput screening of multimetallic catalysts for three-way catalysis

    Research Square · 2023-05-11

    Dinh Son Le, Nhan Nu Thanh Ton, Kalaivani Seenivasan, Patchanee Chammingkwan, Supareak Praserthdam, Toshiaki Taniike

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  12. Machine Learning Descriptors for Data‐Driven Catalysis Study

    Advanced Science · 2023-05-16

    Li-Hui Mou, T.-T. Han, Pieter E. S. Smith, Edward H. Sharman, Jun Jiang

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  13. Fine-tuning channel structure and surface chemistry of stable bismuth-organic frameworks for efficient C2H4 purification through reversely trapping CO2 and C2H2

    Chemical Engineering Journal · 2023-06-30

    Chunlian Hao, Zhencui Ge, Rajamani Krishna, Hao Ren, Houyu Zhu, Yuhua Chi, Wen Jie Zhao, Xiuping Liu, Wenyue Guo

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  14. High-throughput screening of multimetallic catalysts for three-way catalysis

    Science and Technology of Advanced Materials Methods · 2023-11-17

    Son Dinh Le, Nhan Nu Thanh Ton, Kalaivani Seenivasan, Patchanee Chammingkwan, Koichi Higashimine, Supareak Praserthdam, Toshiaki Taniike

    Read original paper ↗ ·

  15. Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

    Communications Chemistry · 2024-01-12

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

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  16. MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models

    Journal of Chemical Information and Modeling · 2024-02-22

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

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  17. Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts

    The Journal of Physical Chemistry C · 2024-12-19

    Yoshiki Hasukawa, Fernando Garcia‐Escobar, KEISUKE G. TAKAHASHI, Lauren Takahashi

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  18. Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering

    Science and Technology of Advanced Materials · 2025-01-21

    Aya Fujiwara, Sunao Nakanowatari, Yohei Cho, Toshiaki Taniike

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  19. 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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  20. Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation

    Machine Learning Science and Technology · 2025-04-16

    Yuta Yahagi, Kiichi Obuchi, Fumihiko Kosaka, Kota Matsui

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  21. 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

    Read original paper ↗ ·

  22. 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

    Read original paper ↗ ·

  23. Designing less noble yet high-performing multimetallic catalysts for catalytic converters using a high-throughput approach

    Catalysis Science & Technology · 2026-01-01

    Son Dinh Le, Nhan Nu Thanh Ton, Panitha Phulkerd, Poulami Mukherjee, Naoto Nagata, Toshiaki Taniike

    Read original paper ↗ ·
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