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RESEARCH PAPER · Current Opinion in Chemical Engineering · 2021

Knowledge extraction in catalysis utilizing design of experiments and machine learning

Blake MacQueen, Rasika Jayarathna, Jochen A. Lauterbach

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Knowledge extraction in catalysis utilizing design of experiments and machine learningOriginalA comprehensive review of Design of experiment (DOE) for water and wastewater treatment application - Key concepts, methodology and contextualized application97Effect of hot-dip tin coating treatment on the CuSn10Pb10/45 steel joint fabricated by diffusion bonding13Experimental and Computational Synergistic Design of Cu and Fe Catalysts for the Reverse Water–Gas Shift: A Review148Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane51Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing40DBD plasma-assisted ethanol steam reforming for green H2 production: Process optimization through response surface methodology (RSM)17Editorial overview: Data-centric catalysis and reaction engineering0Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane15The biosorption of Co(II) by Homalothecium sericeum from aqueous solutions under batch conditions: response surface methodology optimization and Daphnia magna bioassay1Recent advances in ammonia synthesis over ruthenium single-atom-embedded catalysts: A focused review77DoE‐Assisted Development of a 2H−MoS2‐Catalyzed Approach for the Production of Indole Derivatives7Artificial intelligence in catalysis2Experimental discovery of novel ammonia synthesis catalysts via active learning21Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO2 Gas Adsorption into Zeolites3Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design33Design‐of‐Experiments‐Based Optimisation of Vanillin Yield from Kraft Lignin Using Pulse Electrolysis and Thermolysis2From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190Using Machine Learning to Forecast the Conductive Substrate-Supported Heteroatom-Doped Metal Compound Electrocatalysts for Hydrogen Evolution Reaction5Conversion of hazardous waste into thermal conductive polymer: A prediction and guidance from machine learning2Insights into Structure-Activity Relationships between Y Zeolites and their n-C10 Hydrocracking Performances via Machine Learning Approaches6Artificial Intelligence‐assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data1Data-driven design of Ni-based catalysts for CO2 methanation using interpretable machine learning4AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model6Computer-aided hydrogel synthesis for 3D bioprinting: application of design of experiments (DoE), machine learning (ML), and computational fluid dynamics (CFD)3Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes7Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction0Data driven reveals the upgrading mechanism of waste PET electrolysis: Research progress of machine learning in collaborative production of high-value chemicals and green hydrogen energy0Data as the Backbone of Artificial Intelligence: Insights from Heterogeneous Catalysis0Physics-informed machine learning for asphalt mixture dynamic modulus: Balancing high accuracy with stability and rationality0

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  1. A comprehensive review of Design of experiment (DOE) for water and wastewater treatment application - Key concepts, methodology and contextualized application

    Journal of Water Process Engineering · 2022-03-03

    B.C.Y. Lee, Mohd Salim Mahtab, Teck Heng Neo, Izharul Haq Farooqi, Anwar Khursheed

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  2. Effect of hot-dip tin coating treatment on the CuSn10Pb10/45 steel joint fabricated by diffusion bonding

    Journal of Materials Research and Technology · 2022-03-15

    Lixing Sun, Qian Yang, Juntao Zou, Lei Ni, Xuhang Song, Rongyan Zhang, Shuhua Liang

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  3. Experimental and Computational Synergistic Design of Cu and Fe Catalysts for the Reverse Water–Gas Shift: A Review

    ACS Catalysis · 2022-05-26

    Ergys Pahija, Christopher Panaritis, Sergey Gusarov, Jalil Shadbahr, Farid Bensebaa, Gregory Scott Patience, Daria C. Boffito

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  4. 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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  5. Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing

    Chemical Engineering and Processing - Process Intensification · 2022-09-21

    Mauro Bracconi

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  6. DBD plasma-assisted ethanol steam reforming for green H2 production: Process optimization through response surface methodology (RSM)

    International Journal of Hydrogen Energy · 2022-10-19

    Guoqiang Cao, Yue Xiao, Wei‐Min Huang, Chien‐Hua Chen, Jonas Baltrušaitis

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  7. Editorial overview: Data-centric catalysis and reaction engineering

    Current Opinion in Chemical Engineering · 2022-10-29

    Ryan L. Hartman, Lars Christian Grabow

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

    Catalysis Science & Technology · 2023-01-01

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

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  9. The biosorption of Co(II) by Homalothecium sericeum from aqueous solutions under batch conditions: response surface methodology optimization and Daphnia magna bioassay

    Desalination and Water Treatment · 2023-05-01

    Mehtap Tanyol, Volkan Korkmaz, Numan Yıldırım, Gökhan Önder Ergüven, Nagihan M. Karaaslan, Mevlüt Alataş

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  10. Recent advances in ammonia synthesis over ruthenium single-atom-embedded catalysts: A focused review

    Chemical Engineering Journal · 2023-05-15

    Seyed Majid Ghoreishian, Kaveh Shariati, Yun Suk Huh, Jochen A. Lauterbach

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  11. DoE‐Assisted Development of a 2H−MoS2‐Catalyzed Approach for the Production of Indole Derivatives

    ChemSusChem · 2023-07-24

    Giuseppe Gentile, Marc Morant‐Giner, Lucía F. Cardo, Michele Melchionna, Paolo Fornasiero, Maurizio Prato, Giacomo Filippini

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  12. Artificial intelligence in catalysis

    Elsevier eBooks · 2024-01-01

    Srinivas Rangarajan

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  13. Experimental discovery of novel ammonia synthesis catalysts via active learning

    Journal of Materials Chemistry A · 2024-01-01

    Rasika Jayarathna, Thossaporn Onsree, Samuel M. Drummond, Jennifer Naglic, Jochen A. Lauterbach

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  14. Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO2 Gas Adsorption into Zeolites

    ACS Applied Engineering Materials · 2024-02-02

    Carlos Nieto‐Draghi, Benoît Creton, Xavier Martin, Johan Chaniot, Maxime Moreaud

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  15. Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

    Advanced Science · 2024-03-13

    Joel Y. Y. Loh, Andrew Wang, Abhinav Mohan, Athanasios A. Tountas, Abdelaziz Gouda, Alexandra Tavasoli, Geoffrey Alan Ozin

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  16. Design‐of‐Experiments‐Based Optimisation of Vanillin Yield from Kraft Lignin Using Pulse Electrolysis and Thermolysis

    ChemElectroChem · 2024-03-20

    Ann Cathrin Brix, Luiz Otávio Orzari, Olga A. Krysiak, Ieva A. Cechanaviciutè, Lars Banko, Alfred Ludwig, Bruno C. Janegitz, Wolfgang W. Schuhmann

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  17. 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

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  18. Using Machine Learning to Forecast the Conductive Substrate-Supported Heteroatom-Doped Metal Compound Electrocatalysts for Hydrogen Evolution Reaction

    The Journal of Physical Chemistry C · 2024-10-08

    Nana Zhou, Yaling Zhao, Qingzhang Lv, Yahong Chen

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  19. Conversion of hazardous waste into thermal conductive polymer: A prediction and guidance from machine learning

    Journal of Environmental Management · 2024-10-14

    Zhiyi Wang, Zhiyi Wang, Jiming Su, Yijin Feng, Qianqian Xu, Hongru Jiang, Hongru Jiang

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  20. Insights into Structure-Activity Relationships between Y Zeolites and their n-C10 Hydrocracking Performances via Machine Learning Approaches

    CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION) · 2025-04-01

    Qianyi Ma, Hong Nie, Ping Yang, Jianqiang Liu, Hongyi Gao, Wei David Wang, Songtao Dong

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  21. Artificial Intelligence‐assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data

    Unknown journal · 2025-06-23

    Rasika Jayarathna, Seyed Majid Ghoreishian, Rahat Javaid, Azadeh Mehrani, Thossaporn Onsree, Jochen A. Lauterbach

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  22. Data-driven design of Ni-based catalysts for CO2 methanation using interpretable machine learning

    Molecular Catalysis · 2025-08-30

    Lamara Maciel dos Santos, Dulce Maria de Araújo Melo, Rodolfo L.B.A. Medeiros, Ângelo A.S. Oliveira, Willian A. S. Farias, Rebecca A.B.N. Santiago, Renata Martins Braga

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  23. AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model

    Journal of environmental chemical engineering · 2025-11-01

    Tingfa Zhou, Peng Gui, Chao Hu, Weihua Xiao, Dong Liu, Zhiping Wang

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  24. Computer-aided hydrogel synthesis for 3D bioprinting: application of design of experiments (DoE), machine learning (ML), and computational fluid dynamics (CFD)

    Materials Advances · 2026-01-01

    Minh Hien Nguyen, Gia Huy Duong, Hoang Cac Le, Hoang Cac Tien Le, Minh Hien Nguyen, Vinh‐Dat Vuong, Thi Pham Tan

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  25. Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes

    ACS Engineering Au · 2026-01-28

    Mehmet Erdem Günay, Ramazan Yıldırım

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  26. Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction

    Composites Science and Technology · 2026-05-07

    Yan Hu, Senlin Tian, Yingjie Li

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  27. Data driven reveals the upgrading mechanism of waste PET electrolysis: Research progress of machine learning in collaborative production of high-value chemicals and green hydrogen energy

    Renewable and Sustainable Energy Reviews · 2026-07-31

    Jun Deng, Xuejun Zhu, Yuanchuan Ren, Cheng Wang, Nanqi Ren

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  28. Data as the Backbone of Artificial Intelligence: Insights from Heterogeneous Catalysis

    ACS Catalysis · 2026-08-10

    Dongjae Shin, Ruchika Mahajan, Zan Lian, Jake A. Heinlein, Matteo Cargnello, Christopher J. Tassone, Kirsten T. Winther

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  29. Physics-informed machine learning for asphalt mixture dynamic modulus: Balancing high accuracy with stability and rationality

    Engineering Applications of Artificial Intelligence · 2026-08-12

    Jiahao Sheng, Li Liu, Zhaohui Liu, Da Yong Yang, Yu Su

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