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RESEARCH PAPER · ACS ES&T Engineering · 2021

Evaluation of a Data-Driven, Machine Learning Approach for Identifying Potential Candidates for Environmental Catalysts: From Database Development to Prediction

Yulong Chen, Rong Li, Hongri Suo, Chongxuan Liu

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17 direct citing papers in this saved network. Explore the next layer with second-order citations.

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Evaluation of a Data-Driven, Machine Learning Approach for Identifying Potential Candidates for Environmental Catalysts: From Database Development to PredictionOriginalRecommendations for Advancing FAIR and Open Data Standards in the Water Treatment Community15Knowledge extraction in catalysis utilizing design of experiments and machine learning29Machine learning for design principles for single atom catalysts towards electrochemical reactions116Development of Catalyst Grading System for Diesel Hydrodesulfurization Using Machine Learning Techniques1Bridging the complexity gap in computational heterogeneous catalysis with machine learning280Iterative Approach of Experiment–Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO x Selective Reduction Catalysts22Cross-scale models for iron oxides bioreduction rates8Machine Learning-Accelerated Disentanglement of Activity–Selectivity Trade-off of Multielement Oxide Denitration Catalysts13A machine learning framework for accelerating the development of highly efficient methanol synthesis catalysts20Data‐Driven Analysis of Ni‐Catalyzed Semihydrogenations of Alkynes6Reaction Mechanism Revisited: Oxidation of Propylene on a Supported Palladium Catalyst0Based on machine learning prediction and experimental verification: The optimization of NH3-SCR performance on Ce/Co-modified MnNi catalysts8Machine Learning‐Driven Innovations in Energy and Environmental Catalysis: A Minireview0Machine learning-guided design of energy-related catalysts from nanoparticles to single-atom sites7Multitask Learning Reveals Shared Descriptors Governing Activity and Selectivity in Catalytic Nitrate Reduction0A method for optimizing catalyst preparation conditions based on machine learning and genetic algorithm0Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches0

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  1. Recommendations for Advancing FAIR and Open Data Standards in the Water Treatment Community

    ACS ES&T Engineering · 2021-11-03

    Amanda N. Quay, Peter S. Fiske, Meagan S. Mauter

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  2. 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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  3. 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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  4. Development of Catalyst Grading System for Diesel Hydrodesulfurization Using Machine Learning Techniques

    Journal of the Japan Petroleum Institute · 2022-06-30

    Takayuki Kurogi, Mayumi Etou, Rei Hamada, Shingo Sakai

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  5. Bridging the complexity gap in computational heterogeneous catalysis with machine learning

    Nature Catalysis · 2023-02-23

    Tianyou Mou, Hemanth Somarajan Pillai, Siwen Wang, Mingyu Wan, Xue Han, Neil M. Schweitzer, Fanglin Che, Hongliang Xin

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  6. Iterative Approach of Experiment–Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO x Selective Reduction Catalysts

    Environmental Science & Technology · 2023-07-02

    Yulong Chen, Jia Feng, Xin Wang, Cheng Zhang, Dongfang Ke, Huiyan Zhu, Shuai Wang, Hongri Suo, Chongxuan Liu

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  7. Cross-scale models for iron oxides bioreduction rates

    Journal of Hydrology · 2023-07-19

    Huiyan Zhu, Shuai Wang, Kun Gao, Chongxuan Liu

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  8. Machine Learning-Accelerated Disentanglement of Activity–Selectivity Trade-off of Multielement Oxide Denitration Catalysts

    ACS ES&T Engineering · 2024-02-08

    Yi Dong, Mingchu Ran, Xiao Zhang, Saisai Lin, Haitao Zhao, Yang Yang, Shaojun Liu, Chenghang Zheng, Xiang Gao

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  9. A machine learning framework for accelerating the development of highly efficient methanol synthesis catalysts

    Journal of Energy Chemistry · 2025-01-13

    Weixian Li, Yi Dong, Mingchu Ran, Saisai Lin, Peng Liu, Hao Song, Jun‐Dong Yi, Chaoyang Zhu, Zhifu Qi, Chenghang Zheng, Xiao Zhang, Xiang Gao

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  10. Data‐Driven Analysis of Ni‐Catalyzed Semihydrogenations of Alkynes

    Advanced Synthesis & Catalysis · 2025-01-27

    Miguel Martinez-Fernandez, Md Bin Yeamin, David Dalmau, Jorge Juan Carbó, Albert Poater, Juan V. Alegre‐Requena

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  11. Reaction Mechanism Revisited: Oxidation of Propylene on a Supported Palladium Catalyst

    Langmuir · 2025-04-29

    Hitoshi Mikami, Hiroto Tsuchiya, Nobutaka Maeda

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  12. Based on machine learning prediction and experimental verification: The optimization of NH3-SCR performance on Ce/Co-modified MnNi catalysts

    Journal of Hazardous Materials · 2025-12-23

    Yue Zhu, Fengyu Gao, Yaxin Niu, Tingkai Xiong, Xinyu Xu, Honghong Yi, Shunzheng Zhao, Yuansong Zhou, Ya Wang, Xiaolong Tang

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  13. Machine Learning‐Driven Innovations in Energy and Environmental Catalysis: A Minireview

    ChemistrySelect · 2026-01-30

    Jia Liu, Daguo Wu, Longlong Ma, Lungang Chen

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  14. Machine learning-guided design of energy-related catalysts from nanoparticles to single-atom sites

    Communications Chemistry · 2026-03-24

    Zhongbao Hu, Zhoukun Wang, Yunyi Peng, Zhen Gao, Chanyuan Jin, Zechao C. Zhuang, Jian‐Feng Li

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  15. Multitask Learning Reveals Shared Descriptors Governing Activity and Selectivity in Catalytic Nitrate Reduction

    Environmental Science & Technology · 2026-05-28

    Md. Mahjib Hossain, Rabbi Sikder, Meng Ji, Tao Ye

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  16. A method for optimizing catalyst preparation conditions based on machine learning and genetic algorithm

    Molecular Catalysis · 2026-09-12

    Junhui Ao, Wei Wang, Ziyang Wang, Qianhong qiu, Changming Du

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  17. Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches

    AIChE Journal · 2026-09-27

    Farough Agin, Jules Thibault, Clémence Fauteux‐Lefebvre

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