Computational design of quinone electrolytes for redox flow batteries using high-throughput machine learning and theoretical calculations

Aug 5, 2026

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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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Redox flow batteries and electrolyte requirements

Aqueous organic redox flow batteries and quinone tuning

Computational design approach

Molecular generation and physics-based predictions

Machine learning and structure–property modeling

Screening results and implications

Reproducing experimental performance and discovering new electrolytes

TYPE Original Research PUBLISHED 06 January 2023 DOI 10.3389/fceng.2022.1086412 OPEN ACCESS EDITED BY Jesus Flores Cerrillo, Praxair, United States REVIEWED BY Q. Peter He, Auburn University, United States Yushan Zhu, Beijing University of Chemical Technology, China *CORRESPONDENCE Diannan Lu, [ludiannan@mail.tsinghua.edu.cn](ludiannan@mail.tsinghua.edu.cn) SPECIALTY SECTION This article was submitted to Computational Methods in Chemical Engineering, a section of the journal Frontiers in Chemical Engineering RECEIVED 01 November 2022 ACCEPTED 22 December 2022 PUBLISHED 06 January 2023 CITATION Wang 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. Front. Chem. Eng. 4:1086412 . doi: 10.3389/fceng.2022.1086412 COPYRIGHT © 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. Computational design of quinone electrolytes for redox ﬂow batteries using high-throughput machine learning and theoretical calculations Fei Wang 1, Jipeng Li 2, Zheng Liu 1, Tong Qiu 1, Jianzhong Wu 3 and Diannan Lu 1* 1 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 Molecular 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. KEYWORDS quinones, redox ﬂow battery, machine learning, solvation free energy, HOMO-LUMO gap Introduction Large-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

FAQs

What is the main goal of the computational workflow in this study?

To systematically evaluate and design quinone-based organic electrolytes for redox flow batteries by combining machine learning with quantum-mechanical and theoretical calculations.

Which molecular properties are predicted to relate to redox performance and solubility?

HOMO–LUMO gaps are used to reflect redox potential differences, while solvation free energies are used to account for aqueous solubility.

How large is the screening effort and what new electrolytes are identified?

The platform screens 100,000 disubstituted quinone molecules and identifies new electrolyte candidates consistent with the behavior of high-performance cathode materials observed experimentally.

Aug 5, 2026

Upload by Jiven