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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2017

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes

Sung‐Jin Kim, Adrián Jinich, Alán Aspuru‐Guzik

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

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42 direct citing works

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery ElectrolytesOriginalMachine learning for the structure–energy–property landscapes of molecular crystals214Tuning of the degree of charge transfer and the electronic properties in organic binary compounds by crystal engineering: a perspective200Progress and prospects of next-generation redox flow batteries336A Bayesian Approach to Predict Solubility Parameters94Machine learning for renewable energy materials312Integrating Electrochemical and Statistical Analysis Tools for Molecular Design and Mechanistic Understanding46Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation78Molecular modeling of organic redox‐active battery materials30Artificial neural networks for predicting charge transfer coupling50Electrochemistry and Production Optimization of Naturally Occuring Quinones for Flow Battery Technology0Machine learning for advanced energy materials208A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries82Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage260Generation of Computational Data Sets for Machine Learning Applied to Battery Materials0Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries65Large variability and complexity of isothermal solubility for a series of redox-active phenothiazines9Machine learning for flow batteries: opportunities and challenges58Performance Predictors for Organic Cathodes of Lithium-Ion Battery23Machine learning for a sustainable energy future517SOMAS: a platform for data-driven material discovery in redox flow battery development21High-Throughput Virtual Screening of Quinones for Aqueous Redox Flow Batteries: Status and Perspectives18The Application of Novel Functional Materials to Machine Learning0Machine Learning for FB Electrolyte Screening1N‐Alkylated Pyridoxal Derivatives as Negative Electrolyte Materials for Aqueous Organic Flow Batteries: Computational Screening**8Machine learning based feature engineering for thermoelectric materials by design38Will we ever be able to accurately predict solubility?54Computational Method for Optimal Electrolyte Screening Using Bayesian Optimization and Physics Based Battery Model0Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning25Accelerating battery innovation: AI-powered molecular discovery58Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects51Discovery and Design of Electroactive Molecules for Aqueous Redox Flow Batteries0Artificial Intelligence in the Discovery and Design of Ionic Liquids for Thermal Energy Applications0AI‐Accelerated Design and Modeling of Organic Electrochemical Energy Materials: From Redox‐Active Molecules to Polymer Electrolytes0Nonaqueous Redox Flow Batteries: Opportunities and Challenges for Sustainable Energy Storage0

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  1. Machine learning for the structure–energy–property landscapes of molecular crystals

    Chemical Science · 2017-12-12

    Félix Musil, Sandip De, Jack Yang, Josh E. Campbell, Graeme Matthew Day, Michele Ceriotti

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  2. Tuning of the degree of charge transfer and the electronic properties in organic binary compounds by crystal engineering: a perspective

    Journal of Materials Chemistry C · 2017-12-17

    Hui Jiang, Peng Hu, Jun Ye, Keke K. Zhang, Yi Long, Wenping Hu, Christian Kloc

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  3. Progress and prospects of next-generation redox flow batteries

    Energy storage materials · 2018-06-15

    Changkun Zhang, Leyuan Zhang, Yu Ding, Sangshan Peng, Xuelin Guo, Yu Zhao, Gaohong He, Guihua Yu

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  4. A Bayesian Approach to Predict Solubility Parameters

    Advanced Theory and Simulations · 2018-09-10

    Benjamín Sánchez-Lengeling, Loı̈c M. Roch, José Darío Perea, Stefan Langner, Christoph Josef Brabec, Alán Aspuru‐Guzik

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  5. Machine learning for renewable energy materials

    Journal of Materials Chemistry A · 2019-01-01

    Geun Ho Gu, Juhwan Noh, I. P. Kim, Yousung Jung

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  6. Integrating Electrochemical and Statistical Analysis Tools for Molecular Design and Mechanistic Understanding

    Accounts of Chemical Research · 2020-01-10

    Sophia G. Robinson, Matthew S. Sigman

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  7. Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation

    Journal of Chemical Theory and Computation · 2020-03-02

    WooSeok Jeong, Samuel J. Stoneburner, Daniel S. King, Ruye Li, Andrew Walker, Roland H. Lindh, Laura Gagliardi

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  8. Molecular modeling of organic redox‐active battery materials

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2020-07-26

    Rocco Peter Fornari, Piotr de Silva

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  9. Artificial neural networks for predicting charge transfer coupling

    The Journal of Chemical Physics · 2020-12-04

    Chun-I Wang, Ignasius Joanito, Chang‐Feng Lan, Chao‐Ping Hsu

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  10. Electrochemistry and Production Optimization of Naturally Occuring Quinones for Flow Battery Technology

    Unknown journal · 2021-01-01

    Sebastian Birkedal Kristensen

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  11. Machine learning for advanced energy materials

    Energy and AI · 2021-01-24

    Liu Yun, Oladapo Christopher Esan, Zhefei Pan, Liang An

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  12. A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries

    Applied Energy · 2021-06-04

    Shuaibin Wan, Xiongwei Liang, Haoran Jiang, Jing Sun, Ned Djilali, Tianshou S. Zhao

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  13. Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

    Process Safety and Environmental Protection · 2021-08-17

    Daniel Rangel-Martínez, Krishna D.P. Nigam, Luis Ricardez‐Sandoval

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  14. Generation of Computational Data Sets for Machine Learning Applied to Battery Materials

    Unknown journal · 2021-09-03

    Arghya Bhowmik, Felix T. Bölle, Ivano E. Castelli, Jin Hyun Chang, Juan Maria García Lastra, Nicolai Rask Mathiesen, Alexander Sougaard Tygesen, Tejs Vegge

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  15. Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

    Advanced Energy Materials · 2021-11-23

    Arghya Bhowmik, Maitane Berecibar, Montse Casas‐Cabanas, Gábor Cśanyi, Robert Dominko, Kersti Hermansson, M. Rosa Palacín, Helge Sören Stein, Tejs Vegge

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  16. Large variability and complexity of isothermal solubility for a series of redox-active phenothiazines

    Materials Advances · 2022-01-01

    Anton Sameera Perera, Thilini Malsha Suduwella, N. Harsha Attanayake, Rahul Kant Jha, William L. Eubanks, Ilya A. Shkrob, Chad M. Risko, Aman Preet Kaur, Susan A. Odom

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  17. Machine learning for flow batteries: opportunities and challenges

    Chemical Science · 2022-01-01

    Tianyu Li, Changkun Zhang, Xianfeng Li

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  18. Performance Predictors for Organic Cathodes of Lithium-Ion Battery

    ACS Applied Energy Materials · 2022-01-21

    Kosuke Sakano, Yasuhiko Igarashi, Hiroaki Imai, Shuntaro Miyakawa, Takaya Saito, Yoshiki Takayanagi, Koji Nishiyama, Yuya Oaki

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  19. Machine learning for a sustainable energy future

    Nature Reviews Materials · 2022-10-18

    Zhenpeng Yao, Yanwei Lum, Andrew K. Johnston, L.M. Mejía-Mendoza, Xin Zhou, Yonggang Wen, Alán Aspuru‐Guzik, Edward Hartley Sargent, Zhi Wei Seh

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  20. SOMAS: a platform for data-driven material discovery in redox flow battery development

    Scientific Data · 2022-12-01

    Peiyuan Gao, Amity Andersen, Jonathan Sepulveda, Gihan Panapitiya, Aaron Hollas, Emily Saldanha, Vijayakumar Murugesan, Wei Wang

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  21. High-Throughput Virtual Screening of Quinones for Aqueous Redox Flow Batteries: Status and Perspectives

    Batteries · 2022-12-28

    Abhishek Khetan

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  22. The Application of Novel Functional Materials to Machine Learning

    Springer eBooks · 2023-01-01

    Humaira Rashid Khan, Fahd Sikandar Khan, Javeed Akhtar

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  23. Machine Learning for FB Electrolyte Screening

    Unknown journal · 2023-01-06

    Laura‐Sophie Berg, Jan Hamaekers, Astrid Maaß

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  24. 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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  25. Machine learning based feature engineering for thermoelectric materials by design

    Digital Discovery · 2024-01-01

    U. S. Vaitesswar, Daniil Bash, Tan Huang, Jose Recatala‐Gomez, Tianqi Deng, Shuo‐Wang Yang, Xiaonan Wang, Kedar Hippalgaonkar

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  26. Will we ever be able to accurately predict solubility?

    Scientific Data · 2024-03-18

    Pierre Llompart, Claire Minoletti, Shamkhal Baybekov, Dragos Horvath, Gilles Marcou, Alexandre A. Varnek

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  27. Computational Method for Optimal Electrolyte Screening Using Bayesian Optimization and Physics Based Battery Model

    Journal of The Electrochemical Society · 2024-06-03

    Vamsi Krishna Garapati, Naga Neehar Dingari, Mahesh Mynam, Beena Rai

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  28. Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning

    Journal of Cheminformatics · 2024-10-28

    Zeqing Bao, Gary Tom, Austin Cheng, Jeffrey Watchorn, Alán Aspuru‐Guzik, Christine J. Allen

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  29. Accelerating battery innovation: AI-powered molecular discovery

    Chemical Society Reviews · 2025-01-01

    Yuchen Gao, Xiang Chen, Yu‐Hang Yuan, Yao‐Peng Chen, Yi-Lin Niu, Nan Yao, Yan-Bin Gao, W.Z. Li, Qiang Zhang

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  30. 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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  31. Discovery and Design of Electroactive Molecules for Aqueous Redox Flow Batteries

    ChemEngineering · 2026-04-21

    Qi Zhang, Li Zhang, Xinkuan Zhao, Ke Xu, Zili Chen, Yanliang Ji

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  32. Artificial Intelligence in the Discovery and Design of Ionic Liquids for Thermal Energy Applications

    Journal of Chemical Information and Modeling · 2026-08-28

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  33. AI‐Accelerated Design and Modeling of Organic Electrochemical Energy Materials: From Redox‐Active Molecules to Polymer Electrolytes

    Macromolecular Materials and Engineering · 2026-09-01

    Zhan-Yun Zhang, Giannis Savvas, Jichen Li, Yi Zhou, T.T. Le, Daniel Brandell, Rocío Mercado, Chao Zhang

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  34. Nonaqueous Redox Flow Batteries: Opportunities and Challenges for Sustainable Energy Storage

    Chemical Reviews · 2026-10-12

    Tao Wang, Yuheng Xia, Zhengyu Ju, Zhitao Zhao, Miao Chen, Mingjin Cui, Yu Ding, Guihua Yu

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