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RESEARCH PAPER · Environmental Science & Technology · 2023

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

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

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Iterative Approach of Experiment–Machine Learning for Efficient Optimization of Environmental Catalysts: An Example of NO x Selective Reduction CatalystsOriginalLeveraging machine learning in porous media89Predicting and understanding photocatalytic CO2 reduction reaction with IR spectroscopy-based interpretable machine learning framework15Machine learning-based techno-econo-environmental analysis of CO2-to-olefins process for screening the optimal catalyst and hydrogen color18Machine learning and text mining approaches to design selective catalyst reduction synthesis routes5Data‐Driven Analysis of Ni‐Catalyzed Semihydrogenations of Alkynes6C–H Aminoalkylation of 5-Membered Heterocycles: Influence of Descriptors, Data Set Size, and Data Quality on the Predictiveness of Machine Learning Models and Expansion of the Substrate Space Beyond 1,3-Azoles1Fast prediction of odor concentration along pig manure chain based on machine learning: Monitoring 20 instead of over 100 odorous substances3A review of experimental approaches, trends and opportunities in plasma-based gas conversion research13Iterative Machine Learning-Guided Discovery of Transition Metal Compounds as Catalysts for Li–CO 2 and Li–Air Batteries6Catalyst Deactivation in the Abatement of Atmospheric Pollutants: Origin, Resistance, and Regeneration59Data-augmented machine learning improves water treatment design: Precise prediction of PPCPs reaction with reactive radicals9Machine Learning-Guided Design of Catalysts with SO 2 Resistance for Low-Temperature NH 3 –SCR Reaction7Machine-Learning-Guided Discovery of CH 4 Combustion Catalysts Operating in the Presence of SO 22Meta-Fusion deep heterogeneous network coupled with NSGA-II for Multi-Objective optimization of cement kiln SNCR De-NOx process0Potential, challenges and opportunities of machine learning model for plasma-based gas conversion: a critical review0Multitask Learning Reveals Shared Descriptors Governing Activity and Selectivity in Catalytic Nitrate Reduction0Machine learning accelerates discovery of La-doped MnNiOx catalysts for low-temperature SO2-tolerant NOx reduction0An interpretable grid-resolution machine learning framework for predicting biofilm detachment0Mechanism-aware temperature-resolved machine learning and experimental validation for optimization of Cu-based bimetallic catalysts in NO reduction by CO0Accelerating SCR Catalyst Screening via DeepMD and Bayesian Optimization Reveals a Cross-Chemistry VOx Redox Bottleneck0Descriptor‐Based Zero‐Shot Learning for Predicting Catalytic Constituent Elements0Data driven low carbon design of FeCo catalysts for ciprofloxacin degradation: Bridging causal machine learning and atomistic insights0

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  1. Leveraging machine learning in porous media

    Journal of Materials Chemistry A · 2024-01-01

    Mostafa Delpisheh, Benyamin Ebrahimpour, Abolfazl Fattahi, Majid Siavashi, Hamed Mir, Hossein Mashhadimoslem, Mohammad Ali Abdol, Mina Ghorbani, Javad Shokri, Daniel Niblett, Khabat Khosravi, Shayan Rahimi, Seyed Mojtaba Alirahmi, Haoshui Yu, Ali Elkamel, Vahid Niasar, Mohamed Mamlouk

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  2. Predicting and understanding photocatalytic CO2 reduction reaction with IR spectroscopy-based interpretable machine learning framework

    PNAS Nexus · 2024-08-27

    Yanxia Wang, Yanjuan Sun, Xinyan Liu, Fan Dong

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  3. Machine learning-based techno-econo-environmental analysis of CO2-to-olefins process for screening the optimal catalyst and hydrogen color

    Energy · 2024-10-19

    Qingchun Yang, Zhou Jianlong, Runjie Bao, Dongwen Rong, Dawei Zhang

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  4. Machine learning and text mining approaches to design selective catalyst reduction synthesis routes

    Catalysis Science & Technology · 2025-01-01

    Shuyuan Li, Chenyu Huang, Yunjiang Zhang, Jing Li, Shaorui Sun

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  5. 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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  6. C–H Aminoalkylation of 5-Membered Heterocycles: Influence of Descriptors, Data Set Size, and Data Quality on the Predictiveness of Machine Learning Models and Expansion of the Substrate Space Beyond 1,3-Azoles

    The Journal of Organic Chemistry · 2025-02-11

    Stephanie Felten, Cyndi Qixin He, Marion H. Emmert

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  7. Fast prediction of odor concentration along pig manure chain based on machine learning: Monitoring 20 instead of over 100 odorous substances

    Computers and Electronics in Agriculture · 2025-02-25

    Tiantian Cao, Yunhao Zheng, Bin Shang, Qunxin Cong, Qitao Cao, Hongmin Dong

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  8. A review of experimental approaches, trends and opportunities in plasma-based gas conversion research

    Frontiers of Chemical Science and Engineering · 2025-06-25

    Sirui Li, Pranav Arun, Huub van den Bogaard, Thijs van Raak, Changjun Liu, Fausto Gallucci

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  9. Iterative Machine Learning-Guided Discovery of Transition Metal Compounds as Catalysts for Li–CO 2 and Li–Air Batteries

    Journal of the American Chemical Society · 2025-11-07

    Ding Ding, Xiaoqi Zhu, Heyu Xiao, Xinyan Liu, Jianli Cheng, Jun Biao Lu, Bin Wang

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  10. Catalyst Deactivation in the Abatement of Atmospheric Pollutants: Origin, Resistance, and Regeneration

    Chemical Reviews · 2025-11-25

    Jiang Deng, Xiaonan Hu, Alex J. Klaver, Jun Liu, Bin Liu, Linsheng Bai, Ming Xie, Dengsong Zhang

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  11. Data-augmented machine learning improves water treatment design: Precise prediction of PPCPs reaction with reactive radicals

    Water Research · 2025-12-22

    Jiaqi Wu, Yanzhou Ding, Chengfei Zhu, Qian Sui, Lei Zhou

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  12. Machine Learning-Guided Design of Catalysts with SO 2 Resistance for Low-Temperature NH 3 –SCR Reaction

    Environmental Science & Technology · 2026-01-14

    Huazhen Chang, Xinyi Miao, Haohui Chen, Lei Qiu, Guanglei Li, Chizhong Wang, Junhua Li, Bing Li, Lei Ma

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  13. Machine-Learning-Guided Discovery of CH 4 Combustion Catalysts Operating in the Presence of SO 2

    Journal of the American Chemical Society · 2026-04-10

    Yuan Jing, Kah Wei Ting, Junxian Qin, Katsuya Yamada, Daichi Tajima, Akihiko Anzai, Shinya Mine, Ryo Toyoshima, Hiroshi Kondoh, Ichigaku Takigawa, Ken‐ichi Shimizu, Takashi Toyao

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  14. Meta-Fusion deep heterogeneous network coupled with NSGA-II for Multi-Objective optimization of cement kiln SNCR De-NOx process

    Chemical Engineering Science · 2026-05-13

    Liteng An, Xiaochen Hao, Zhipeng Zhang, Xunian Yang, Lizhao Zheng, Songlin Zhang

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  15. Potential, challenges and opportunities of machine learning model for plasma-based gas conversion: a critical review

    ENGINEERING Chemical Engineering · 2026-05-15

    Jiayin Li, Xinpei Lu, Sirui Li, Annemie Bogaerts

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  16. 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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  17. Machine learning accelerates discovery of La-doped MnNiOx catalysts for low-temperature SO2-tolerant NOx reduction

    Applied Catalysis A General · 2026-06-29

    Zhengyan Zhu, Hao Shen, Yongjie Shen, Lupeng Han, Fuli Wang, Wenqiang Qu, Runfang Mao, Pan Zhang, Minjie Li, Dengsong Zhang

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  18. An interpretable grid-resolution machine learning framework for predicting biofilm detachment

    Water Research · 2026-08-12

    Shuai Wang, Yuming Sun, Yongsheng Chen, Xing Hua Xie

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  19. Mechanism-aware temperature-resolved machine learning and experimental validation for optimization of Cu-based bimetallic catalysts in NO reduction by CO

    Journal of Hazardous Materials · 2026-08-17

    Wenhao DUAN, Huanran Wang, Hao Chen, Yan Zhao, Xiang Li, Xianchun Li

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  20. Accelerating SCR Catalyst Screening via DeepMD and Bayesian Optimization Reveals a Cross-Chemistry VOx Redox Bottleneck

    Catalysis Letters · 2026-08-24

    Chengzheng Tong, Caixia Liu, Qingling Liu, Mingfa Yao, Hu Wang

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  21. Descriptor‐Based Zero‐Shot Learning for Predicting Catalytic Constituent Elements

    ChemistrySelect · 2026-09-01

    Jun Maki, Hiromasa Kaneko

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  22. Data driven low carbon design of FeCo catalysts for ciprofloxacin degradation: Bridging causal machine learning and atomistic insights

    Separation and Purification Technology · 2026-09-23

    Shengwen Zhou, Rui Yang, Chenran Zhou, Guixian Zhu, Xingwu Lu, Xiuwen Cheng

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