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RESEARCH PAPER · ACS Omega · 2022

Predicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine Learning

Siddharth Ghule, Soumya Ranjan Dash, Sayan Bagchi, Kavita Joshi, Kumar Vanka

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42 direct citing works

Predicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine LearningOriginalMachine learning for flow batteries: opportunities and challenges58Machine Learning for Flow Battery Systems0DFT calculation, a practical tool to predict the electrochemical behaviour of organic electrolytes in aqueous redox flow batteries15N‐Alkylated Pyridoxal Derivatives as Negative Electrolyte Materials for Aqueous Organic Flow Batteries: Computational Screening**8Quinones for Aqueous Organic Redox Flow Battery: A Prospective on Redox Potential, Solubility, and Stability60Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks27Static theoretical investigations of organic redox active materials for redox flow batteries6Predicting Redox Potentials by Graph-Based Machine Learning Methods1Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials8Predicting Redox Potentials by Graph-Based Machine Learning Methods1Choice of the Right Supporting Electrolyte in Electrochemical Reductions: A Principal Component Analysis16Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery28Predicting redox potentials by graph‐based machine learning methods10AI for organic and polymer synthesis35Rapid discovery of Transglutaminase 2 inhibitors for celiac disease with boosting ensemble machine learning13Machine learning for the redox potential prediction of molecules in organic redox flow battery15Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects50Charge-regulated reduction potential of phenazine enabling electrochemiluminescence for monitoring riboflavin in beans germination1Integrating Experiments and Machine Learning Modeling to Assess the Half-Wave Potentials of Antibiotics1Unveiling redox potential behavior in electrolytes: A machine learning approach to Li-ion coordination effects1PredPotS: web tool for predicting one-electron standard reduction potentials for organic molecules in aqueous phase1Chemical modulation of redox polymers for high-performance aqueous redox flow batteries1Role of Structural, Pharmacokinetic, and Energy Properties in the High-Throughput Prediction of Redox Potentials for Organic Molecules with Experimental Calibration0A hybrid DFT–machine learning approach for fast HOMO–LUMO gap prediction in naphthalene diimide MOF linkers3High‐Energy‐Density Redox Flow Batteries: Mechanisms, Design Strategies, and Recent Progress2

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  1. Machine learning for flow batteries: opportunities and challenges

    Chemical Science · 2022-01-01

    Tianyu Li, Changkun Zhang, Xianfeng Li

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  2. Machine Learning for Flow Battery Systems

    Engineering Applications of Computational Methods · 2023-01-01

    Akeel Abbas Shah, Puiki Leung, Qian Feng Xu, Pang‐Chieh Sui, Wei W. Xing

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  3. DFT calculation, a practical tool to predict the electrochemical behaviour of organic electrolytes in aqueous redox flow batteries

    Journal of Power Sources · 2023-02-24

    Juan Asenjo‐Pascual, Ivan Salmeron-Sànchez, Pablo Mauleón, Maddalen Agirre, Ana Catarina Lopes, Oihane Zugazua, Eduardo Sánchez‐Díez, Juan Ramón Avilés‐Moreno, P. Ocón

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  4. N‐Alkylated Pyridoxal Derivatives as Negative Electrolyte Materials for Aqueous Organic Flow Batteries: Computational Screening**

    Chemistry - A European Journal · 2023-05-19

    Andrea Hamza, Flóra Boróka Németh, Ádám Madarász, Anton A. Nechaev, Petri M. Pihko, Pekka Peljo, Imre Pápai

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  5. Quinones for Aqueous Organic Redox Flow Battery: A Prospective on Redox Potential, Solubility, and Stability

    Advanced Materials Interfaces · 2023-07-17

    Fuead Hasan, Vivekananda Mahanta, Ahmed Adel A. Abdelazeez

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  6. Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks

    Journal of Chemical Theory and Computation · 2023-07-18

    Rostislav Fedorov, Ganna Gryn’ova

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  7. Static theoretical investigations of organic redox active materials for redox flow batteries

    Progress in Energy · 2023-11-02

    Aleksandr Zaichenko, Andreas J. Achazi, Simon Kunz, Hermann A. Wegner, Jürgen Janek, Doreen Mollenhauer

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  8. Predicting Redox Potentials by Graph-Based Machine Learning Methods

    ChemRxiv · 2023-11-09

    Linlin Jia, Éric A. G. Brémond, Larissa Zaida, Benoît Gaüzère, Vincent Tognetti, Laurent Joubert

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  9. Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials

    Machine Learning Science and Technology · 2024-03-01

    Siwoo Lee, Stefan Heinen, Danish Khan, O. Anatole von Lilienfeld

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  10. Predicting Redox Potentials by Graph-Based Machine Learning Methods

    ChemRxiv · 2024-03-18

    Linlin Jia, Éric A. G. Brémond, Larissa Zaida, Benoît Gaüzère, Vincent Tognetti, Laurent Joubert

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  11. Choice of the Right Supporting Electrolyte in Electrochemical Reductions: A Principal Component Analysis

    Journal of the American Chemical Society · 2024-05-24

    Florian Mast, Maximilian M. Hielscher, Tom Wirtanen, Max Erichsen, Jürgen Gauß, Gregor Diezemann, Siegfried R. Waldvogel

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  12. Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

    ChemElectroChem · 2024-06-10

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  13. Predicting redox potentials by graph‐based machine learning methods

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    Linlin Jia, Éric A. G. Brémond, Larissa Zaida, Benoît Gaüzère, Vincent Tognetti, Laurent Joubert

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  14. AI for organic and polymer synthesis

    Science China Chemistry · 2024-06-26

    Xin Hong, Qi Yang, Kuangbiao Liao, Jianfeng Pei, Mao Chen, Fanyang Mo, Hua Lu, Wen-Bin Zhang, Haisen Zhou, Jiaxiao Chen, Lebin Su, Shuo‐Qing Zhang, Siyuan Liu, Xu Huang, Yizhou Sun, Yuxiang Wang, Zexi Zhang, Zhunzhun Yu, Sanzhong Luo, Xuefeng Fu, Shu‐Li You

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  15. Rapid discovery of Transglutaminase 2 inhibitors for celiac disease with boosting ensemble machine learning

    Computational and Structural Biotechnology Journal · 2024-10-16

    Ibrahim Wichka, Pin‐Kuang Lai

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  16. Machine learning for the redox potential prediction of molecules in organic redox flow battery

    Journal of Power Sources · 2024-12-16

    Peiyuan Gao, Didem Kochan, Yuhang Tang, Xiu Yang, Emily Saldanha

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  17. Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects

    Chemistry of Materials · 2025-02-28

    Souvik Manna, Poulami Paul, Surya Sekhar Manna, Sandeep Das, Biswarup Pathak

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  18. Charge-regulated reduction potential of phenazine enabling electrochemiluminescence for monitoring riboflavin in beans germination

    Analytica Chimica Acta · 2025-09-25

    Hao-Tian Zhu, Mi Chen, Ai‐Jun Wang, Pei-Xin Yuan, Jiu‐Ju Feng

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  19. Integrating Experiments and Machine Learning Modeling to Assess the Half-Wave Potentials of Antibiotics

    ACS ES&T Engineering · 2025-10-03

    Wenxuan Chen, Benjamin Korth, Dafang Fu, Anja Worrich

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  20. Unveiling redox potential behavior in electrolytes: A machine learning approach to Li-ion coordination effects

    Materials Today Energy · 2025-11-01

    Deshuai Yang, Pu Zhang, Yu Xiong, Tairan Wang, Qianqian Wang, Cuili Zhang, Lang Wang, Shengbo Lu, Tracy Chenmin Liu, Shihan Qi, Weiguo Huang, Jing-Jing Liu, Guannan Zhu, Jun Fan

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  21. PredPotS: web tool for predicting one-electron standard reduction potentials for organic molecules in aqueous phase

    npj Computational Materials · 2025-12-07

    Flóra Boróka Németh, Andrea Hamza, Beatrix Tugyi, Maya El-Ali, Luca Szegletes, Ádám Madarász, Imre Pápai

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  22. Chemical modulation of redox polymers for high-performance aqueous redox flow batteries

    Journal of Electroanalytical Chemistry · 2026-01-08

    Nesreen Almadani, Ata-ur-Rehman, Safyan Akram Khan, S. Shujath Ali, Muhammad Mansha, Muhammad Nawaz Tahir, Majad Khan

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  23. Role of Structural, Pharmacokinetic, and Energy Properties in the High-Throughput Prediction of Redox Potentials for Organic Molecules with Experimental Calibration

    ACS Omega · 2026-02-23

    Apurva Kalia, Vladimir O. Porokhin, Benjamin T. Blackburn, Robyn A. C. Alba, Caroline Marie Ajo-Franklin, Emily E. Mevers, Soha Hassoun

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  24. A hybrid DFT–machine learning approach for fast HOMO–LUMO gap prediction in naphthalene diimide MOF linkers

    Materials Chemistry and Physics · 2026-03-14

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  25. High‐Energy‐Density Redox Flow Batteries: Mechanisms, Design Strategies, and Recent Progress

    Chemistry - A European Journal · 2026-04-10

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