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RESEARCH PAPER · ACS ES&T Water · 2022

Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds

Kai Zhang, Huichun Judy Zhang

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Machine Learning Modeling of Environmentally Relevant Chemical Reactions for Organic CompoundsOriginalMachine Learning Application In Modeling Organic Pollutant Adsorption on Carbonaceous Materials: A Comprehensive Review with Statistical Insights1Tree-Based Ensemble Machine Learning Model For Predictive and Interpretability Analysis of Nitrate Reduction by Zero-Valent Iron Rate Constant and Product Conversion0Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds36Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH 3 /H/O Radicals12Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures10Tree-based ensemble machine learning model for nitrate reduction by zero-valent iron12Advancing the Comprehension of Iron Ions with Different Valences in Anammox-Based Processes: Insight into the Response and Mechanism Based on Prediction and Classification Machine Learning Models13Advances in the analysis of disinfection by-products with mass spectrometry: Sample preparation and target/non-target screening17An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening25Machine learning framework for predicting cytotoxicity and identifying toxicity drivers of disinfection byproducts23Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments15Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning8How machine learning boosts the understanding of organic pollutant adsorption on carbonaceous materials: A comprehensive review with statistical insights20Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach78Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge25Advanced Oxidation Processes for Activated Carbon Regeneration: From Fundamental to Application14Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system25Predicting reaction rate constants of organic compounds with oxidants in the atmospheric aqueous-phase through multi-task learning3Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment44Advancements in electrocoagulation for oily wastewater treatment: Mechanisms, efficiency, and applications22Machine Vision and Image Processing for Drones0Transforming PFAS management: A critical review of machine learning applications for enhanced monitoring and treatment25Leveraging big data to elucidate the impact of heavy metal nanoparticles on anammox processes in wastewater treatment3From Model Development to Mitigation: Machine Learning for Predicting and Minimizing Iodinated Trihalomethanes in Water Treatment28Unveiling the Role of Wetland Strategies in Antibiotic Risk Reduction across China by Machine Learning8QCD-MLP: A Machine-Learning Framework for Predicting Transformation Products of Organic Micropollutants in Radical-Mediated Systems2PDL1-inhi.predictor: a two-tiered machine learning framework for predicting and prioritizing PDL1 inhibitors2Data handling and preprocessing in chemical datasets0Hydrolysis Reaction Rate Prediction Using Machine Learning: WaterDRoP1From Molecules to the Field: Iron Oxides Controlling Pharmaceutical Fate and Remediation0How to Build Machine-Learning Models for Molecular Science: A Step-by-Step, Annotated Tutorial0Harnessing Machine Learning to Enhance UV-based Advanced Oxidation Processes for Sustainable Micropollutant Abatement0Toward autonomous chemical discovery: agentic AI in organic synthesis and nanochemistry0

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  1. Machine Learning Application In Modeling Organic Pollutant Adsorption on Carbonaceous Materials: A Comprehensive Review with Statistical Insights

    SSRN Electronic Journal · 2023-01-01

    Zichu Wang, Jianwei Yu, Qi Wang, Fan Yang, Chunmiao Wang, Min Yang

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  2. Tree-Based Ensemble Machine Learning Model For Predictive and Interpretability Analysis of Nitrate Reduction by Zero-Valent Iron Rate Constant and Product Conversion

    SSRN Electronic Journal · 2023-01-01

    Donghwi Jung, Nurul Alvia Istiqomah

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  3. Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds

    Journal of Hazardous Materials · 2023-06-27

    Ying He, Guohong Liu, Song Hu, Xiaohong Wang, Jianbo Jia, Hongyu Zhou, Xiliang Yan

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  4. Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH 3 /H/O Radicals

    Journal of Chemical Information and Modeling · 2023-08-10

    Yu Zhang, Jinhui Yu, Hongwei Song, Minghui Yang

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  5. Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures

    Materials · 2023-09-21

    Piotr Cysewski, Maciej Przybyłek, Tomasz Jeliński

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  6. Tree-based ensemble machine learning model for nitrate reduction by zero-valent iron

    Journal of Water Process Engineering · 2023-09-22

    Nurul Alvia Istiqomah, Donghwi Jung, Jeehyeong Khim

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  7. Advancing the Comprehension of Iron Ions with Different Valences in Anammox-Based Processes: Insight into the Response and Mechanism Based on Prediction and Classification Machine Learning Models

    ACS ES&T Water · 2023-10-10

    Zhicheng Jiang, Xinxin Xu, Yuhang He, Ming Zeng, Meng Zhang, Wei Liu, Nan Wu

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  8. Advances in the analysis of disinfection by-products with mass spectrometry: Sample preparation and target/non-target screening

    TrAC Trends in Analytical Chemistry · 2024-02-22

    Jianfa Gao, Foon Yin Lai, Cristina Postigo, Mengting Yang

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  9. An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening

    The Science of The Total Environment · 2024-02-23

    An Su, Yingying Cheng, Chengwei Zhang, Yun‐Fang Yang, Yuanbin She, Krishna Rajan

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  10. Machine learning framework for predicting cytotoxicity and identifying toxicity drivers of disinfection byproducts

    Journal of Hazardous Materials · 2024-03-07

    Rabbi Sikder, Huichun Judy Zhang, Peng Gao, Tao Ye

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  11. Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

    ACS ES&T Water · 2024-03-08

    Huichun Judy Zhang, Carla A. Ng

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  12. Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

    The Journal of Physical Chemistry A · 2024-03-13

    Yu Zhang, Min Xia, Hongwei Song, Minghui Yang

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  13. How machine learning boosts the understanding of organic pollutant adsorption on carbonaceous materials: A comprehensive review with statistical insights

    Separation and Purification Technology · 2024-05-11

    Zichu Wang, Qi Wang, Fan Yang, Chunmiao Wang, Min Yang, Jianwei Yu

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  14. Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach

    Environmental Science & Technology · 2024-07-10

    Jinqi Jiang, Xiang Xiang, Qinhao Zhou, Lichang Zhou, Xinqi Bi, Samir Kumar Khanal, Zongping Wang, Guanghao Chen, Gang Guo

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  15. Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge

    Environmental Science & Technology · 2024-07-11

    Yu‐Shu Cheng, Kai Zhang, Kuan Z. Huang, Huichun Judy Zhang

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  16. Advanced Oxidation Processes for Activated Carbon Regeneration: From Fundamental to Application

    ACS ES&T Water · 2024-07-16

    Jian Liu, Shaohua Chen, Xin Ye, Jiawei Chen, Jianzhi Huang

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  17. Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system

    The Science of The Total Environment · 2024-08-15

    Ying Yu, Md. Mahjib Hossain, Rabbi Sikder, Zhenguo Qi, Lixin Huo, Ruya Chen, Wenyue Dou, Baoyou Shi, Tao Ye

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  18. Predicting reaction rate constants of organic compounds with oxidants in the atmospheric aqueous-phase through multi-task learning

    Atmospheric Environment · 2024-08-27

    Linghao Gu, Zhongming Chen

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  19. Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment

    TrAC Trends in Analytical Chemistry · 2024-09-21

    François Nkinahamira, Anqi Feng, Lijie Zhang, Lijie Zhang, Hongwei Rong, Pamphile Ndagijimana, Dabin Guo, Baihui Cui, Huichun Judy Zhang

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  20. Advancements in electrocoagulation for oily wastewater treatment: Mechanisms, efficiency, and applications

    Journal of Water Process Engineering · 2024-10-16

    Hansa Muvel, Manoj Kumar Jindal, Pradip Kumar Tewari, Vikky Anand

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  21. Machine Vision and Image Processing for Drones

    Advances in computational intelligence and robotics book series · 2024-12-13

    Kwok Tai Chui, Varsha Arya, Akshat Gaurav, Shavi Bansal, Ritika Bansal

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  22. Transforming PFAS management: A critical review of machine learning applications for enhanced monitoring and treatment

    Journal of Water Process Engineering · 2025-01-15

    Md Hasan-Ur Rahman, Rabbi Sikder, Tanvir Alam Tonmoy, Md. Mahjib Hossain, Tao Ye, Nirupam Aich, Venkataramana R. Gadhamshetty

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  23. Leveraging big data to elucidate the impact of heavy metal nanoparticles on anammox processes in wastewater treatment

    Journal of Environmental Management · 2025-04-16

    Yiqun Hong, Zhenguo Chen, Zehua Huang, Zehua Huang, Chunying Zheng, Junxing Liu, Chenxi Zeng, Xiangfa Kong, Chao Zhang, Mingzhi Huang

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  24. From Model Development to Mitigation: Machine Learning for Predicting and Minimizing Iodinated Trihalomethanes in Water Treatment

    Environmental Science & Technology · 2025-06-03

    Md. Mahjib Hossain, Rabbi Sikder, Guanghui Hua, Tao Ye

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  25. Unveiling the Role of Wetland Strategies in Antibiotic Risk Reduction across China by Machine Learning

    Environmental Science & Technology · 2025-07-23

    Lei Chen, Junxiang Shi, Danni Wu, Ying Bo Zhu, Jonathan M. Adams, Jichun Wu, Xiaohui Chen, Hongyan Guo

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  26. QCD-MLP: A Machine-Learning Framework for Predicting Transformation Products of Organic Micropollutants in Radical-Mediated Systems

    ACS ES&T Water · 2025-09-18

    Dilhani Senevinanda, Dhimas Dwinandha, Mohamed Elsamadony, Jibao Liu, Mikito Fujinami, Manabu Fujii

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  27. PDL1-inhi.predictor: a two-tiered machine learning framework for predicting and prioritizing PDL1 inhibitors

    Bioorganic Chemistry · 2025-11-01

    Huiyan Ying, Junting Liu, Weikaixin Kong, Xiangwei Xu

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  28. Data handling and preprocessing in chemical datasets

    Elsevier eBooks · 2026-01-01

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  29. Hydrolysis Reaction Rate Prediction Using Machine Learning: WaterDRoP

    Environmental Science & Technology · 2026-04-21

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  30. From Molecules to the Field: Iron Oxides Controlling Pharmaceutical Fate and Remediation

    Accounts of Materials Research · 2026-06-23

    Muhammad Usman, Tao Luo, Wei Cheng, Huichun Judy Zhang, Jean‐François Boily, Khalil Hanna

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  31. How to Build Machine-Learning Models for Molecular Science: A Step-by-Step, Annotated Tutorial

    ACS Environmental Au · 2026-08-13

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  32. Harnessing Machine Learning to Enhance UV-based Advanced Oxidation Processes for Sustainable Micropollutant Abatement

    Environmental Science & Technology · 2026-08-19

    Bohan Li, Zhongyan Zhang, Xinyuan Yi, Chii Shang, Yingzheng Fan, Jinfeng Wang, Ran Yin, Xinkun Ren

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  33. Toward autonomous chemical discovery: agentic AI in organic synthesis and nanochemistry

    Results in Engineering · 2026-09-24

    Boppudi Hari Babu, Pullaiah Chowdary Vutla, Jagadeesh Mahimaluru, Bryce Ke Jun Chong, Srinivasulu Kambam

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