[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125544-en":3,"doc-seo-125544-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125544,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Computational design of quinone electrolytes for redox flow batteries using high-throughput machine learning and theoretical calculations","Computational procedure is proposed to systematically evaluate organic redox-active species for redox flow batteries by integrating machine learning, quantum-mechanical calculations, and classical density functional theory. From building blocks of benzoquinone, naphthoquinone, and anthraquinone, 1,517 small quinone molecules are generated and assessed using physics-based predictions of HOMO–LUMO gaps and solvation free energies to capture redox potential differences and aqueous solubility. The workflow combines quantitative structure–property relationship analysis and graph-network modeling to reproduce high-performance cathode electrolytes and identify new candidates by screening 100,000 disubstituted quinones, supporting improved structure–function understanding for all-organic active materials in RFBs.","TYPE Original Research PUBLISHED 06 January 2023  \nDOI 10.3389/fceng.2022.1086412  \nOPEN ACCESS  \nEDITED BY  \nJesus Flores Cerrillo, Praxair, United States  \nREVIEWED BY  \nQ. Peter He,  \nAuburn University, United States Yushan Zhu,  \nBeijing University of Chemical Technology, China  \n*CORRESPONDENCE  \nDiannan Lu,  \n [ludiannan@mail.tsinghua.edu.cn](ludiannan@mail.tsinghua.edu.cn)  \nSPECIALTY SECTION  \nThis article was submitted to Computational Methods in Chemical Engineering,  \na section of the journal  \nFrontiers in Chemical Engineering  \nRECEIVED 01 November 2022  \nACCEPTED 22 December 2022  \nPUBLISHED 06 January 2023  \nCITATION  \nWang F, Li J, Liu Z, Qiu T, Wu J and Lu D (2023), Computational design of quinone electrolytes for redox flow batteries using high-throughput machine learning and theoretical calculations.  \nFront. Chem. Eng. 4:1086412 .  \ndoi: 10.3389/fceng.2022.1086412  \nCOPYRIGHT  \n© 2023 Wang, Li, Liu, Qiu, Wu and Lu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nComputational design of quinone electrolytes for redox flow batteries using high-throughput machine learning and theoretical calculations  \nFei Wang 1, Jipeng Li 2, Zheng Liu 1, Tong Qiu 1, Jianzhong Wu 3 and Diannan Lu 1*  \n1 Department of Chemical Engineering, Tsinghua University, Beijing, China, 2School of Materials Science and Engineering, Hainan University, Haikou, China, 3Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA, United States  \nMolecular design of redox-active materials with higher solubility and greater redox potential windows is instrumental in enhancing the performance of redox flow batteries Here we propose a computational procedure for a systematic evaluation of organic redox-active species by combining machine learning, quantum-mechanical, and classical density functional theory calculations. 1,517 small quinone molecules were generated from the building blocks of benzoquinone, naphthoquinone, and anthraquinone with different substituent groups. The physics-based methods were used to predict HOMO-LUMO gaps and solvation free energies that account for the redox potential differences and aqueous solubility, respectively. The highthroughput calculations were augmented with the quantitative structure-property relationship analyses and machine learning/graph network modeling to evaluate the materials ’ overall behavior. The computational procedure was able to reproduce high-performance cathode electrolyte materials consistent with experimental observations and identify new electrolytes for RFBs by screening 100,000 disubstituted quinone molecules, the largest library of redox-active quinone molecules ever investigated. The efficient computational platform may facilitate abetter understanding of the structure-function relationship of quinone molecules and advance the design and application of all-organic active materials for RFBs.  \nKEYWORDS  \nquinones, redox flow battery, machine learning, solvation free energy, HOMO-LUMO gap  \nIntroduction  \nLarge-scale, stationary energy storage techniques are imperative for the widespread applicability of green energy such as wind and solar power (Hasewend et al., 2020) . A redox flow battery (RFB) is an electrochemical energy storage device (Eckroad and Gyuk, 2003), in which catholyte and anolyte are stored in separate external tanks and transported to the battery for energy conversions. The RFB power is determined by the capacity of electrodes while its energy density depends on the volume, the composition, and the concentration of the redox-active e","cbCaiu5twCasA1T7","https://ap.wps.com/l/cbCaiu5twCasA1T7","pdf",2456137,1,10,"English","en",105,"# Introduction\n## Redox flow batteries and electrolyte requirements\n## Aqueous organic redox flow batteries and quinone tuning\n# Computational design approach\n## Molecular generation and physics-based predictions\n## Machine learning and structure–property modeling\n# Screening results and implications\n## Reproducing experimental performance and discovering new electrolytes","[{\"question\":\"What is the main goal of the computational workflow in this study?\",\"answer\":\"To systematically evaluate and design quinone-based organic electrolytes for redox flow batteries by combining machine learning with quantum-mechanical and theoretical calculations.\"},{\"question\":\"Which molecular properties are predicted to relate to redox performance and solubility?\",\"answer\":\"HOMO–LUMO gaps are used to reflect redox potential differences, while solvation free energies are used to account for aqueous solubility.\"},{\"question\":\"How large is the screening effort and what new electrolytes are identified?\",\"answer\":\"The platform screens 100,000 disubstituted quinone molecules and identifies new electrolyte candidates consistent with the behavior of high-performance cathode materials observed experimentally.\"}]","Computational design of quinone electrolytes for redox flow batteries using high-throughput machine learning and theoretical calculations | 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