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28-08-2023 | Review

Structural design of organic battery electrode materials: from DFT to artificial intelligence

Authors: Ting-Ting Wu, Gao-Le Dai, Jin-Jia Xu, Fang Cao, Xiao-Hong Zhang, Yu Zhao, Yu-Min Qian

Published in: Rare Metals | Issue 10/2023

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Abstract

Redox-active organic materials are emerging as the new playground for the design of new exciting battery materials for rechargeable batteries because of the merits including structural diversity and tunable electrochemical properties that are not easily accessible for the inorganic counterparts. More importantly, the sustainability developed by using naturally abundant chemical elements (C, H, N, O and S) makes them as an ideal alternative material for Li-ion batteries (LIBs). However, the identification and screening of proper organic materials is still challenging in the past decades. Assisted by the artificial intelligence, this review will give a summary of the theoretical design aspects of redox-active organic materials from density-functional theory (DFT) to machine learning (ML) methods in the past two decades, with a particular emphasis on the calculation method to predict the chemical/electrochemical stability and reversibility. This review will also analyze and discuss the challenges and perspectives for the development of organic battery materials.

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[1]

Williams DL, Byrne JJ, Driscoll JS. A high energy density lithium/dichloroisocyanuric acid battery system. J Electrochem Soc. 1969;116(1):2. https://doi.org/10.1149/1.2411755.CrossRef

[2]

Rajagopalan R, Tang Y, Jia C, Ji X, Wang H. Understanding the sodium storage mechanisms of organic electrodes in sodium ion batteries: issues and solutions. Energy Environ Sci. 2020;13(6):1568. https://doi.org/10.1039/C9EE03637G.CrossRef

[3]

Ghosh S, Makeev MA, Macaggi ML, Qi ZM, Wang HY, Rajput NN, Martha SK, Pol VG. Dipotassium terephthalate as promising potassium storing anode with DFT calculations. Mater Today Energy. 2020;17:100454. https://doi.org/10.1016/j.mtener.2020.100454.CrossRef

[4]

Kapaev RR, Troshin PA. Organic-based active electrode materials for potassium batteries: status and perspectives. J Mater Chem A. 2020;8(34):17296. https://doi.org/10.1039/D0TA04741D.CrossRef

[5]

Luder J, Manzhos S. First-principle insights into molecular design for high-voltage organic electrode materials for Mg based batteries. Front Chem. 2020;8:83. https://doi.org/10.3389/fchem.2020.00083.CrossRef

[6]

Han C, Li H, Li Y, Zhu J, Zhi C. Proton-assisted calcium-ion storage in aromatic organic molecular crystal with coplanar stacked structure. Nat Commun. 2021;12(1):2400. https://doi.org/10.1038/s41467-021-22698-9.CrossRef

[7]

Gao YJ, Li GF, Wang F, Chu J, Yu P, Wang BS, Zhan H, Song ZP. A high-performance aqueous rechargeable zinc battery based on organic cathode integrating quinone and pyrazine. Energy Storage Mater. 2021;40:31. https://doi.org/10.1016/j.ensm.2021.05.002.CrossRef

[8]

Qin K, Huang J, Holguin K, Luo C. Recent advances in developing organic electrode materials for multivalent rechargeable batteries. Energy Environ Sci. 2020;13(11):3950. https://doi.org/10.1039/D0EE02111C.CrossRef

[9]

Xie J, Zhang Q. Recent progress in multivalent metal (Mg, Zn, Ca, and Al) and metal-ion rechargeable batteries with organic materials as promising electrodes. Small. 2019;15(15):1805061. https://doi.org/10.1002/smll.201805061.CrossRef

[10]

Poizot P, Gaubicher J, Renault S, Dubois L, Liang Y, Yao Y. Opportunities and challenges for organic electrodes in electrochemical energy storage. Chem Rev. 2020;120(14):6490. https://doi.org/10.1021/acs.chemrev.9b00482.CrossRef

[11]

Muench S, Wild A, Friebe C, Haupler B, Janoschka T, Schubert US. Polymer-based organic batteries. Chem Rev. 2016;116(16):9438. https://doi.org/10.1021/acs.chemrev.6b00070.CrossRef

[12]

Hager MD, Esser B, Feng X, Schuhmann W, Theato P, Schubert US. Polymer-based batteries—flexible and thin energy storage systems. Adv Mater. 2020;32(39):2000587. https://doi.org/10.1002/adma.202000587.CrossRef

[13]

Xie Y, Zhang K, Yamauchi Y, Oyaizu K, Jia Z. Nitroxide radical polymers for emerging plastic energy storage and organic electronics: fundamentals, materials, and applications. Mater Horiz. 2021;8(3):803. https://doi.org/10.1039/D0MH01391A.CrossRef

[14]

Le T, Epa VC, Burden FR, Winkler DA. Quantitative structure–property relationship modeling of diverse materials properties. Chem Rev. 2012;112(5):2889. https://doi.org/10.1021/cr200066h.CrossRef

[15]

Olson GB. Computational design of hierarchically structured materials. Science. 1997;277(5330):1237. https://doi.org/10.1126/science.277.5330.1237.CrossRef

[16]

Agrawal A, Choudhary A. Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL Mater. 2016;4(5):053208. https://doi.org/10.1063/1.4946894.CrossRef

[17]

Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu CW, Qiu J, Hua K, Su W, Wu J, Xu H, Han Y, Fu C, Yin Z, Liu M, Roepman R, Dietmann S, Virta M, Kengara F, Zhang Z, Zhang L, Zhao T, Dai J, Yang J, Lan L, Luo M, Liu Z, An T, Zhang B, He X, Cong S, Liu X, Zhang W, Lewis JP, Tiedje JM, Wang Q, An Z, Wang F, Zhang L, Huang T, Lu C, Cai Z, Wang F, Zhang J. Artificial intelligence: a powerful paradigm for scientific research. The Innovation. 2021;2(4):100179. https://doi.org/10.1016/j.xinn.2021.100179.CrossRef

[18]

Song Z, Qian Y, Zhang T, Otani M, Zhou H. Poly(benzoquinonyl sulfide) as a high-energy organic cathode for rechargeable li and na batteries. Adv Sci. 2015;2(9):1500124. https://doi.org/10.1002/advs.201500124.CrossRef

[19]

Dai G, Wang X, Qian Y, Niu Z, Zhu X, Ye J, Zhao Y, Zhang X. Manipulation of conjugation to stabilize n redox-active centers for the design of high-voltage organic battery cathode. Energy Storage Mater. 2019;16:236. https://doi.org/10.1016/j.ensm.2018.06.005.CrossRef

[20]

Dai G, Liu Y, Niu Z, He P, Zhao Y, Zhang X, Zhou H. The design of quaternary nitrogen redox center for high-performance organic battery materials. Matter. 2019;1(4):945. https://doi.org/10.1016/j.matt.2019.05.009.CrossRef

[21]

Li H, Wu T, Chen Y, Liu Y, Jiang Z, Zhang X, Dai G, Zhao Y. Self-crosslinked herringbone dihydrophenazine derivatives for high performance organic batteries. Compos Commun. 2021;28:100947. https://doi.org/10.1016/j.coco.2021.100947.CrossRef

[22]

Dai G, He Y, Niu Z, He P, Zhang C, Zhao Y, Zhang X, Zhou H. A dual-ion organic symmetric battery constructed from phenazine-based artificial bipolar molecules. Angew Chem Int Ed. 2019;58(29):9902. https://doi.org/10.1002/anie.201901040.CrossRef

[23]

Dai G, Wu T, Chen H, Zhao Y. Quaternary nitrogen redox centers for battery materials. Curr Opin Electrochem. 2021;29:100745. https://doi.org/10.1016/j.coelec.2021.100745.CrossRef

[24]

Zhao Y, Ding Y, Li Y, Peng L, Byon HR, Goodenough JB, Yu G. A chemistry and material perspective on lithium redox flow batteries towards high-density electrical energy storage. Chem Soc Rev. 2015;44(22):7968. https://doi.org/10.1039/C5CS00289C.CrossRef

[25]

Zhang C, Niu Z, Peng S, Ding Y, Zhang L, Guo X, Zhao Y, Yu G. Phenothiazine-based organic catholyte for high-capacity and long-life aqueous redox flow batteries. Adv Mater. 2019;31(24):1901052. https://doi.org/10.1002/adma.201901052.CrossRef

[26]

Ding Y, Zhao Y, Li Y, Goodenough JB, Yu G. A high-performance all-metallocene-based, non-aqueous redox flow battery. Energy Environ Sci. 2017;10(2):491. https://doi.org/10.1039/C6EE02057G.CrossRef

[27]

Xiao Y, Miara LJ, Wang Y, Ceder G. Computational screening of cathode coatings for solid-state batteries. Joule. 2019;3(5):1252. https://doi.org/10.1016/j.joule.2019.02.006.CrossRef

[28]

Tabor DP, Gómez-Bombarelli R, Tong L, Gordon RG, Aziz MJ, Aspuru-Guzik A. Mapping the frontiers of quinone stability in aqueous media: implications for organic aqueous redox flow batteries. J Mater Chem A. 2019;7(20):12833. https://doi.org/10.1039/C9TA03219C.CrossRef

[29]

Fornari RP, de Silva P. A computational protocol combining dft and cheminformatics for prediction of ph-dependent redox potentials. Molecules. 2021;26(13):3978.CrossRef

[30]

Chen Y, Sun S, Wang X, Shi Q. Study of lithium migration pathways in the organic electrode materials of Li-battery by dispersion-corrected density functional theory. J Phys Chem C. 2015;119(46):25719. https://doi.org/10.1021/acs.jpcc.5b07978.CrossRef

[31]

Sun PK, Lu HL, Zhang WW, Wu HJ, Sun SR, Liu XY. Poly(ethylene terephthalate): rubbish could be low cost anode material of lithium ion battery. Solid State Ionics. 2018;317:164. https://doi.org/10.1016/j.ssi.2018.01.024.CrossRef

[32]

Garrido Torres JA, Jennings PC, Hansen MH, Boes JR, Bligaard T. Low-scaling algorithm for nudged elastic band calculations using a surrogate machine learning model. Phys Rev Lett. 2019;122(15):156001. https://doi.org/10.1103/PhysRevLett.122.156001.CrossRef

[33]

Jinnouchi R, Karsai F, Kresse G. On-the-fly machine learning force field generation: application to melting points. Phys Rev B. 2019;100(1):014105. https://doi.org/10.1103/PhysRevB.100.014105.CrossRef

[34]

Sivaraman G, Guo J, Ward L, Hoyt N, Williamson M, Foster I, Benmore C, Jackson N. Automated development of molten salt machine learning potentials: application to LiCl. J Phys Chem Lett. 2021;12(17):4278. https://doi.org/10.1021/acs.jpclett.1c00901.CrossRef

[35]

Stephens PJ, Devlin FJ, Chabalowski CF, Frisch MJ. Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields. J Phys Chem. 1994;98(45):11623. https://doi.org/10.1021/j100096a001.CrossRef

[36]

Niu Z, Wu H, Lu Y, Xiong S, Zhu X, Zhao Y, Zhang X. Orbital-dependent redox potential regulation of quinone derivatives for electrical energy storage. RSC Adv. 2019;9(9):5164. https://doi.org/10.1039/C8RA09377F.CrossRef

[37]

Sun M, Dougherty AW, Huang B, Li Y, Yan CH. Accelerating atomic catalyst discovery by theoretical calculations-machine learning strategy. Adv Energy Mater. 2020;10(12):1903949. https://doi.org/10.1002/aenm.201903949.CrossRef

[38]

Sun M, Wong HH, Wu T, Dougherty AW, Huang B. Stepping out of transition metals: activating the dual atomic catalyst through main group elements. Adv Energy Mater. 2021;11(30):2101404. https://doi.org/10.1002/aenm.202101404.CrossRef

[39]

Sun M, Wong HH, Wu T, Dougherty AW, Huang B. Entanglement of spatial and energy segmentation for C₁ pathways in CO₂ reduction on carbon skeleton supported atomic catalysts. Adv Energy Mater. 2022;12(14):2103781. https://doi.org/10.1002/aenm.202103781.CrossRef

[40]

Sun M, Wu T, Dougherty AW, Lam M, Huang B, Li Y, Yan CH. Self-validated machine learning study of graphdiyne-based dual atomic catalyst. Adv Energy Mater. 2021;11(13):2003796. https://doi.org/10.1002/aenm.202003796.CrossRef

[41]

Sun J, Ruzsinszky A, Perdew JP. Strongly constrained and appropriately normed semilocal density functional. Phys Rev Lett. 2015;115(3):036402. https://doi.org/10.1103/PhysRevLett.115.036402.CrossRef

[42]

Bartók AP, Yates JR. Regularized scan functional. J Chem Phys. 2019;150(16):161101. https://doi.org/10.1063/1.5094646.CrossRef

[43]

Furness JW, Kaplan AD, Ning J, Perdew JP, Sun J. Accurate and numerically efficient r²SCAN meta-generalized gradient approximation. J Phys Chem Lett. 2020;11(19):8208. https://doi.org/10.1021/acs.jpclett.0c02405.CrossRef

[44]

Caldeweyher E, Ehlert S, Hansen A, Neugebauer H, Spicher S, Bannwarth C, Grimme S. A generally applicable atomic-charge dependent london dispersion correction. J Chem Phys. 2019;150(15):154122. https://doi.org/10.1063/1.5090222.CrossRef

[45]

Ehlert S, Huniar U, Ning J, Furness JW, Sun J, Kaplan AD, Perdew JP, Brandenburg JG. r²SCAN-D4: dispersion corrected meta-generalized gradient approximation for general chemical applications. J Chem Phys. 2021;154(6):061101. https://doi.org/10.1063/5.0041008.CrossRef

[46]

Grimme S, Hansen A, Ehlert S, Mewes JM. r²SCAN-3c: A swiss army knife composite electronic-structure method. J Chem Phys. 2021;154(6):064103. https://doi.org/10.1063/5.0040021.CrossRef

[47]

Ali BA, Allam NK. A first-principles roadmap and limits to design efficient supercapacitor electrode materials. Phys Chem Chem Phys. 2019;21(32):17494. https://doi.org/10.1039/c9cp02614b.CrossRef

[48]

Hautier G, Fischer CC, Jain A, Mueller T, Ceder G. Finding nature’s missing ternary oxide compounds using machine learning and density functional theory. Chem Mater. 2010;22(12):3762. https://doi.org/10.1021/cm100795d.CrossRef

[49]

Prentice JCA, Aarons J, Womack JC, Allen AEA, Andrinopoulos L, Anton L, Bell RA, Bhandari A, Bramley GA, Charlton RJ, Clements RJ, Cole DJ, Constantinescu G, Corsetti F, Dubois SM-M, Duff KKB, Escartín JM, Greco A, Hill Q, Lee LP, Linscott E, O’Regan DD, Phipps MJS, Ratcliff LE, Serrano ÁR, Tait EW, Teobaldi G, Vitale V, Yeung N, Zuehlsdorff TJ, Dziedzic J, Haynes PD, Hine NDM, Mostofi AA, Payne MC, Skylaris CK. The onetep linear-scaling density functional theory program. J Chem Phys. 2020;152(17):174111. https://doi.org/10.1063/5.0004445.CrossRef

[50]

Ruddigkeit L, van Deursen R, Blum LC, Reymond JL. Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17. J Chem Inf Model. 2012;52(11):2864. https://doi.org/10.1021/ci300415d.CrossRef

[51]

Hoffmann T, Gastreich M. The next level in chemical space navigation: going far beyond enumerable compound libraries. Drug Discov Today. 2019;24(5):1148. https://doi.org/10.1016/j.drudis.2019.02.013.CrossRef

[52]

Sha W, Li Y, Tang S, Tian J, Zhao Y, Guo Y, Zhang W, Zhang X, Lu S, Cao YC, Cheng S. Machine learning in polymer informatics. InfoMat. 2021;3(4):353. https://doi.org/10.1002/inf2.12167.CrossRef

[53]

Zhang BJ, Zhang YY, Yang XD, Li GP, Zhang SK, Zhang YF, Yu DM, Liu ZS, He G. Isometric thionated naphthalene diimides as organic cathodes for high capacity lithium batteries. Chem Mater. 2020;32(24):10575. https://doi.org/10.1021/acs.chemmater.0c03661.CrossRef

[54]

Yao M, Senoh H, Yamazaki S, Siroma Z, Sakai T, Yasuda K. High-capacity organic positive-electrode material based on a benzoquinone derivative for use in rechargeable lithium batteries. J Power Sources. 2010;195(24):8336. https://doi.org/10.1016/j.jpowsour.2010.06.069.CrossRef

[55]

Ding Y, Guo X, Qian Y, Zhang L, Xue L, Goodenough JB, Yu G. A liquid-metal-enabled versatile organic alkali-ion battery. Adv Mater. 2019;31(11):1806956. https://doi.org/10.1002/adma.201806956.CrossRef

[56]

Liang Y, Zhang P, Chen J. Function-oriented design of conjugated carbonyl compound electrodes for high energy lithium batteries. Chem Sci. 2013;4(3):1330. https://doi.org/10.1039/c3sc22093a.CrossRef

[57]

Lu Y, Hou XS, Miao LC, Li L, Shi RJ, Liu LJ, Chen J. Cyclohexanehexone with ultrahigh capacity as cathode materials for lithium-ion batteries. Angewandte Chemie-Int Edit. 2019;58(21):7020. https://doi.org/10.1002/anie.201902185.CrossRef

[58]

Zhao Q, Wang JB, Lu Y, Li YX, Liang GX, Chen J. Oxocarbon salts for fast rechargeable batteries. Angewandte Chemie-Int Edit. 2016;55(40):12528. https://doi.org/10.1002/anie.201607194.CrossRef

[59]

Song Z, Qian Y, Liu X, Zhang T, Zhu Y, Yu H, Otani M, Zhou H. A quinone-based oligomeric lithium salt for superior Li–organic batteries. Energy Environ Sci. 2014;7(12):4077. https://doi.org/10.1039/C4EE02575J.CrossRef

[60]

Song Z, Qian Y, Gordin ML, Tang D, Xu T, Otani M, Zhan H, Zhou H, Wang D. Polyanthraquinone as a reliable organic electrode for stable and fast lithium storage. Angew Chem Int Ed. 2015;54(47):13947. https://doi.org/10.1002/anie.201506673.CrossRef

[61]

Song Z, Qian Y, Otani M, Zhou H. Stable Li–organic batteries with nafion-based sandwich-type separators. Adv Energy Mater. 2016;6(7):1501780. https://doi.org/10.1002/aenm.201501780.CrossRef

[62]

Dardenne N, Hautier G, Gohy JF, Charlier JC, Rignanese GM. Ab initio calculations of open cell voltage in newly designed ptma-based li-ion organic radical batteries. Comput Mater Sci. 2018;143:27. https://doi.org/10.1016/j.commatsci.2017.10.038.CrossRef

[63]

Dai G, Gao Y, Niu Z, He P, Zhang X, Zhao Y, Zhou H. Dilution of the electron density in the π-conjugated skeleton of organic cathode materials improves the discharge voltage. Chemsuschem. 2020;13(9):2264. https://doi.org/10.1002/cssc.201903502.CrossRef

[64]

Uno B, Kano K, Konse T, Kubota T, Matsuzaki S, Kuboyama A. Origin of the negative shift of half-wave reduction potentials of aromatic polynuclear p-quinones with increasing conjugation. Chem Pharm Bull. 1985;33(12):5155. https://doi.org/10.1248/cpb.33.5155.CrossRef

[65]

Yao M, Senoh H, Araki M, Sakai T, Yasuda K. Organic positive-electrode materials based on dialkoxybenzoquinone derivatives for use in rechargeable lithium batteries. ECS Trans. 2010;28(8):3. https://doi.org/10.1149/1.3490677.CrossRef

[66]

Liu T, Kim KC, Lee B, Chen ZM, Noda S, Jang SS, Lee SW. Self-polymerized dopamine as an organic cathode for Li- and Na-ion batteries. Energy Environ Sci. 2017;10(1):205. https://doi.org/10.1039/c6ee02641a.CrossRef

[67]

Min DJ, Miomandre F, Audebert P, Kwon JE, Park SY. S-tetrazines as a new electrode-active material for secondary batteries. Chemsuschem. 2019;12(2):503. https://doi.org/10.1002/cssc.201802290.CrossRef

[68]

Bachman JE, Curtiss LA, Assary RS. Investigation of the redox chemistry of anthraquinone derivatives using density functional theory. J Phys Chem A. 2014;118(38):8852. https://doi.org/10.1021/jp5060777.CrossRef

[69]

Kim KC, Liu TY, Lee SW, Jang SS. First-principles density functional theory modeling of li binding: thermodynamics and redox properties of quinone derivatives for lithium-ion batteries. J Am Chem Soc. 2016;138(7):2374. https://doi.org/10.1021/jacs.5b13279.CrossRef

[70]

Sieuw L, Lakraychi AE, Rambabu D, Robeyns K, Jouhara A, Borodi G, Morari C, Poizot P, Vlad A. Through-space charge modulation overriding substituent effect: rise of the redox potential at 3.35 V in a lithium-phenolate stereoelectronic isomer. Chem Mater. 2020;32(23):9996. https://doi.org/10.1021/acs.chemmater.0c02989.CrossRef

[71]

Banda H, Damien D, Nagarajan K, Raj A, Hariharan M, Shaijumon MM. Twisted perylene diimides with tunable redox properties for organic sodium-ion batteries. Adv Energy Mater. 2017;7(20):1701316. https://doi.org/10.1002/aenm.201701316.CrossRef

[72]

Cui C, Ji X, Wang PF, Xu GL, Chen L, Chen J, Kim H, Ren Y, Chen F, Yang C, Fan X, Luo C, Amine K, Wang C. Integrating multiredox centers into one framework for high-performance organic Li-ion battery cathodes. ACS Energy Lett. 2020;5(1):224. https://doi.org/10.1021/acsenergylett.9b02466.CrossRef

[73]

Pahlevaninezhad M, Leung P, Velasco PQ, Pahlevani M, Walsh FC, Roberts EPL. Ponce de León C. A nonaqueous organic redox flow battery using multi-electron quinone molecules. J Power Sources. 2021;500:229942. https://doi.org/10.1016/j.jpowsour.2021.229942.CrossRef

[74]

Lee S, Lee K, Ku K, Hong J, Park SY, Kwon JE, Kang K. Utilizing latent multi-redox activity of p-type organic cathode materials toward high energy density lithium-organic batteries. Adv Energy Mater. 2020;10(32):2001635. https://doi.org/10.1002/aenm.202001635.CrossRef

[75]

Zhao LB, Gao ST, He RX, Shen W, Li M. Molecular design of phenanthrenequinone derivatives as organic cathode materials. Chemsuschem. 2018;11(7):1215. https://doi.org/10.1002/cssc.201702344.CrossRef

[76]

Liu Y, Dai G, Chen Y, Wang R, Li H, Shi X, Zhang X, Xu Y, Zhao Y. Effective design strategy of small bipolar molecules through fused conjugation toward 25 V based redox flow batteries. ACS Energy Lett. 2022. https://doi.org/10.1021/acsenergylett.2c00198.CrossRef

[77]

Lambert F, Danten Y, Gatti C, Frayret C. A tool for deciphering the redox potential ranking of organic compounds: a case study of biomass-extracted quinones for sustainable energy. Phys Chem Chem Phys. 2020;22(36):20212. https://doi.org/10.1039/D0CP02045A.CrossRef

[78]

Wu D, Xie Z, Zhou Z, Shen P, Chen Z. Designing high-voltage carbonyl-containing polycyclic aromatic hydrocarbon cathode materials for Li-ion batteries guided by Clar’s theory. J Mater Chem A. 2015;3(37):19137. https://doi.org/10.1039/c5ta05437k.CrossRef

[79]

Ma T, Liu L, Wang J, Lu Y, Chen J. Charge storage mechanism and structural evolution of viologen crystals as the cathode of lithium batteries. Angew Chem Int Ed. 2020;59(28):11533. https://doi.org/10.1002/anie.202002773.CrossRef

[80]

Huang L, Chen Y, Liu Y, Wu T, Li H, Ye J, Dai G, Zhang X, Zhao Y. Π-extended dihydrophenazine-based polymeric cathode material for high-performance organic batteries. ACS Sustain Chem Eng. 2020;8(48):17868. https://doi.org/10.1021/acssuschemeng.0c07314.CrossRef

[81]

Peterson BM, Gannett CN, Melecio-Zambrano L, Fors BP, Abruña H. Effect of structural ordering on the charge storage mechanism of p-type organic electrode materials. ACS Appl Mater Interfaces. 2021;13(6):7135. https://doi.org/10.1021/acsami.0c19622.CrossRef

[82]

Lee K, Serdiuk IE, Kwon G, Min DJ, Kang K, Park SY, Kwon JE. Phenoxazine as a high-voltage p-type redox center for organic battery cathode materials: small structural reorganization for faster charging and narrow operating voltage. Energy Environ Sci. 2020;13(11):4142. https://doi.org/10.1039/D0EE01003K.CrossRef

[83]

Nokami T, Matsuo T, Inatomi Y, Hojo N, Tsukagoshi T, Yoshizawa H, Shimizu A, Kuramoto H, Komae K, Tsuyama H, Yoshida J. Polymer-bound pyrene-4,5,9,10-tetraone for fast-charge and -discharge lithium-ion batteries with high capacity. J Am Chem Soc. 2012;134(48):19694. https://doi.org/10.1021/ja306663g.CrossRef

[84]

Luo C, Xu GL, Ji X, Hou S, Chen L, Wang F, Jiang J, Chen Z, Ren Y, Amine K, Wang C. Reversible redox chemistry of azo compounds for sodium-ion batteries. Angew Chem Int Ed. 2018;57(11):2879. https://doi.org/10.1002/anie.201713417.CrossRef

[85]

Luo C, Borodin O, Ji X, Hou S, Gaskell KJ, Fan XL, Chen J, Deng T, Wang RX, Jiang JJ, Wang CS. Azo compounds as a family of organic electrode materials for alkali-ion batteries. Proc Natl Acad Sci USA. 2018;115(9):2004. https://doi.org/10.1073/pnas.1717892115.CrossRef

[86]

Zhang L, Qian Y, Feng R, Ding Y, Zu X, Zhang C, Guo X, Wang W, Yu G. Reversible redox chemistry in azobenzene-based organic molecules for high-capacity and long-life nonaqueous redox flow batteries. Nat Commun. 2020;11(1):3843. https://doi.org/10.1038/s41467-020-17662-y.CrossRef

[87]

Zu X, Zhang L, Qian Y, Zhang C, Yu G. Molecular engineering of azobenzene-based anolytes towards high-capacity aqueous redox flow batteries. Angew Chem Int Ed. 2020;59(49):22163. https://doi.org/10.1002/anie.202009279.CrossRef

[88]

Kim DJ, Hermann KR, Prokofjevs A, Otley MT, Pezzato C, Owczarek M, Stoddart JF. Redox-active macrocycles for organic rechargeable batteries. J Am Chem Soc. 2017;139(19):6635. https://doi.org/10.1021/jacs.7b01209.CrossRef

[89]

Zhang F, Cheng Y, Niu Z, Ye J, Dai G, Zhang X, Zhao Y. Tailoring the voltage gap of organic battery materials based on a multi-electron redox chemistry. Chem Electro Chem. 2020;7(7):1781. https://doi.org/10.1002/celc.202000279.CrossRef

[90]

Shi R, Liu L, Lu Y, Wang C, Li Y, Li L, Yan Z, Chen J. Nitrogen-rich covalent organic frameworks with multiple carbonyls for high-performance sodium batteries. Nat Commun. 2020;11(1):178. https://doi.org/10.1038/s41467-019-13739-5.CrossRef

[91]

Wang HG, Li Q, Wu Q, Si Z, Lv X, Liang X, Wang H, Sun L, Shi W, Song S. Conjugated microporous polymers with bipolar and double redox-active centers for high-performance dual-ion, organic symmetric battery. Adv Energy Mater. 2021;11(20):2100381. https://doi.org/10.1002/aenm.202100381.CrossRef

[92]

Zhang C, Chen H, Qian Y, Dai G, Zhao Y, Yu G. General design methodology for organic eutectic electrolytes toward high-energy-density redox flow batteries. Adv Mater. 2021;33(15):2008560. https://doi.org/10.1002/adma.202008560.CrossRef

[93]

Yu YX. A dispersion-corrected dft study on adsorption of battery active materials anthraquinone and its derivatives on monolayer graphene and h-BN. J Mater Chem A. 2014;2(23):8910. https://doi.org/10.1039/c4ta00103f.CrossRef

[94]

Wang JB, Zhao Q, Wang GC, Li FJ, Chen J. Enhanced adsorption of carbonyl molecules on graphene via π-Li-π interaction: a first-principle study. Sci China Mater. 2017;60(7):674. https://doi.org/10.1007/s40843-017-9047-0.CrossRef

[95]

Lu Y, Zhao Q, Miao LC, Tao ZL, Niu ZQ, Chen J. Flexible and free-standing organic/carbon nanotubes hybrid films as cathode for rechargeable lithium-ion batteries. J Phys Chem C. 2017;121(27):14498. https://doi.org/10.1021/acs.jpcc.7b04341.CrossRef

[96]

Hu B, DeBruler C, Rhodes Z, Liu TL. Long-cycling aqueous organic redox flow battery (AORFB) toward sustainable and safe energy storage. J Am Chem Soc. 2017;139(3):1207. https://doi.org/10.1021/jacs.6b10984.CrossRef

[97]

Ghosh S, Makeev MA, Qi ZM, Wang HY, Rajput NN, Martha SK, Pol VG. Rapid upcycling of waste polyethylene terephthalate to energy storing disodium terephthalate flowers with dft calculations. ACS Sustain Chem Eng. 2020;8(16):6252. https://doi.org/10.1021/acssuschemeng.9b07684.CrossRef

[98]

Mahmood A, Wang JL. A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection. J Mater Chem A. 2021;9(28):15684. https://doi.org/10.1039/D1TA04742F.CrossRef

[99]

Mahmood A, Irfan A, Wang JL. Developing efficient small molecule acceptors with sp2-hybridized nitrogen at different positions by density functional theory calculations, molecular dynamics simulations and machine learning. Chem A Eur J. 2022;28(2):e202103712. https://doi.org/10.1002/chem.202103712.CrossRef

[100]

Zhang C, Qian Y, Ding Y, Zhang L, Guo X, Zhao Y, Yu G. Biredox eutectic electrolytes derived from organic redox-active molecules: high-energy storage systems. Angew Chem Int Ed. 2019;58(21):7045. https://doi.org/10.1002/anie.201902433.CrossRef

[101]

Wu X, Jin S, Zhang Z, Jiang L, Mu L, Hu YS, Li H, Chen X, Armand M, Chen L, Huang X. Unraveling the storage mechanism in organic carbonyl electrodes for sodium-ion batteries. Sci Adv. 2015;1(8):e1500330. https://doi.org/10.1126/sciadv.1500330.CrossRef

[102]

Chen J, Liu T, Gao L, Qian Y, Liu Y, Kong X. Tuning the solution structure of electrolyte for optimal solid-electrolyte-interphase formation in high-voltage lithium metal batteries. J Energy Chem. 2021;60:178. https://doi.org/10.1016/j.jechem.2021.01.007.CrossRef

[103]

Mahmood A, Irfan A, Wang JL. Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th-based organic solar cells with over 15% efficiency. J Mater Chem A. 2022;10(8):4170. https://doi.org/10.1039/D1TA09762H.CrossRef

[104]

Zhang C, Niu Z, Ding Y, Zhang L, Zhou Y, Guo X, Zhang X, Zhao Y, Yu G. Highly concentrated phthalimide-based anolytes for organic redox flow batteries with enhanced reversibility. Chem. 2018;4(12):2814. https://doi.org/10.1016/j.chempr.2018.08.024.CrossRef

[105]

Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel TD, Duvenaud D, Maclaurin D, Blood-Forsythe MA, Chae HS, Einzinger M, Ha DG, Wu T, Markopoulos G, Jeon S, Kang H, Miyazaki H, Numata M, Kim S, Huang W, Hong SI, Baldo M, Adams RP, Aspuru-Guzik A. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat Mater. 2016;15(10):1120. https://doi.org/10.1038/nmat4717.CrossRef

[106]

Narayanan B, Redfern PC, Assary RS, Curtiss LA. Accurate quantum chemical energies for 133 000 organic molecules. Chem Sci. 2019;10(31):7449. https://doi.org/10.1039/c9sc02834j.CrossRef

[107]

Sun SR, Chen YH, Yu J. High throughput screening of organic electrode materials for lithium battery by theoretical method. J Phys Chem C. 2015;119(46):25770. https://doi.org/10.1021/acs.jpcc.5b08609.CrossRef

[108]

Cheng L, Assary RS, Qu X, Jain A, Ong SP, Rajput NN, Persson K, Curtiss LA. Accelerating electrolyte discovery for energy storage with high-throughput screening. J Phys Chem Lett. 2015;6(2):283. https://doi.org/10.1021/jz502319n.CrossRef

[109]

Crabtree G. The joint center for energy storage research: a new paradigm for battery research and development. AIP Conf Proc. 2015;1652(1):112. https://doi.org/10.1063/1.4916174.CrossRef

[110]

Shen J, Nicolaou CA. Molecular property prediction: recent trends in the era of artificial intelligence. Drug Discov Today Technol. 2019. https://doi.org/10.1016/j.ddtec.2020.05.001.CrossRef

[111]

Cruz C, Molina A, Patil N, Ventosa E, Marcilla R, Mavrandonakis A. New insights into phenazine-based organic redox flow batteries by using high-throughput dft modelling. Sustain Energy Fuels. 2020;4(11):5513. https://doi.org/10.1039/D0SE00687D.CrossRef

[112]

Park JH, Liu TY, Kim KC, Lee SW, Jang SS. Systematic molecular design of ketone derivatives of aromatic molecules for lithium-ion batteries: first-principles dft modeling. Chemsuschem. 2017;10(7):1584. https://doi.org/10.1002/cssc.201601730.CrossRef

[113]

Er S, Suh C, Marshak MP, Aspuru-Guzik A. Computational design of molecules for an all-quinone redox flow battery. Chem Sci. 2015;6(2):885. https://doi.org/10.1039/c4sc03030c.CrossRef

[114]

Schwan S, Schröder D, Wegner HA, Janek J, Mollenhauer D. Substituent pattern effects on the redox potentials of quinone-based active materials for aqueous redox flow batteries. Chemsuschem. 2020;13(20):5480. https://doi.org/10.1002/cssc.202000454.CrossRef

[115]

Jinich A, Sanchez-Lengeling B, Ren H, Harman R, Aspuru-Guzik A. A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315 000 redox reactions. ACS Cent Sci. 2019;5(7):1199. https://doi.org/10.1021/acscentsci.9b00297.CrossRef

[116]

Faber FA, Hutchison L, Huang B, Gilmer J, Schoenholz SS, Dahl GE, Vinyals O, Kearnes S, Riley PF, von Lilienfeld OA. Prediction errors of molecular machine learning models lower than hybrid DFT error. J Chem Theory Comput. 2017;13(11):5255. https://doi.org/10.1021/acs.jctc.7b00577.CrossRef

[117]

Xu SQ, Liang JC, Yu YD, Liu RL, Xu Y, Zhu X, Zhao Y. Machine learning-assisted discovery of high-voltage organic materials for rechargeable batteries. J Phys Chem C. 2021;125(39):21352. https://doi.org/10.1021/acs.jpcc.1c06821.CrossRef

[118]

Li CH, Tabor DP. Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening. J Mater Chem A. 2022. https://doi.org/10.1039/D2TA00743F.CrossRef

[119]

Carvalho RP, Marchiori CFN, Brandell D, Araujo CM. Artificial intelligence driven in-silico discovery of novel organic lithium-ion battery cathodes. Energy Storage Mater. 2022;44:313. https://doi.org/10.1016/j.ensm.2021.10.029.CrossRef

[120]

Allam O, Cho BW, Kim KC, Jang SS. Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries. RSC Adv. 2018;8(69):39414. https://doi.org/10.1039/c8ra07112h.CrossRef

[121]

Allam O, Kuramshin R, Stoichev Z, Cho BW, Lee SW, Jang SS. Molecular structure–redox potential relationship for organic electrode materials: density functional theory–machine learning approach. Mater Today Energy. 2020;17:100482. https://doi.org/10.1016/j.mtener.2020.100482.CrossRef

[122]

Barker J, Berg LS, Hamaekers J, Maass A. Rapid prescreening of organic compounds for redox flow batteries: a graph convolutional network for predicting reaction enthalpies from smiles. Batteries Supercaps. 2021;4(9):1482. https://doi.org/10.1002/batt.202100059.CrossRef

[123]

Vydrov OA, Voorhis TV. Nonlocal van der Waals density functional: the simpler the better. J Chem Phys. 2010;133(24):244103. https://doi.org/10.1063/1.3521275.CrossRef

[124]

Sabatini R, Gorni T, de Gironcoli S. Nonlocal van der Waals density functional made simple and efficient. Phys Rev B. 2013;87(4):041108. https://doi.org/10.1103/PhysRevB.87.041108.CrossRef

[125]

Mahmood A, Wang JL. Machine learning for high performance organic solar cells: current scenario and future prospects. Energy Environ Sci. 2021;14(1):90. https://doi.org/10.1039/D0EE02838J.CrossRef

[126]

Mahmood A, Irfan A, Wang JL. Machine learning for organic photovoltaic polymers: a minireview. Chin J Polym Sci. 2022;40(8):870. https://doi.org/10.1007/s10118-022-2782-5.CrossRef

[127]

Jin C, Nai J, Sheng O, Yuan H, Zhang W, Tao X, Lou XW. Biomass-based materials for green lithium secondary batteries. Energy Environ Sci. 2021;14(3):1326. https://doi.org/10.1039/D0EE02848G.CrossRef

[128]

Liedel C. Sustainable battery materials from biomass. Chemsuschem. 2020;13(9):2110. https://doi.org/10.1002/cssc.201903577.CrossRef

[129]

Liu L, Solin N, Inganäs O. Bio based batteries. Adv Energy Mater. 2021;11(43):2003713. https://doi.org/10.1002/aenm.202003713.CrossRef

[130]

Strietzel C, Sterby M, Huang H, Strømme M, Emanuelsson R, Sjödin M. An aqueous conducting redox-polymer-based proton battery that can withstand rapid constant-voltage charging and sub-zero temperatures. Angew Chem Int Ed. 2020;59(24):9631. https://doi.org/10.1002/anie.202001191.CrossRef

[131]

Ham Y, Fritz NJ, Hyun G, Lee YB, Nam JS, Kim ID, Braun PV, Jeon S. 3D periodic polyimide nano-networks for ultrahigh-rate and sustainable energy storage. Energy Environ Sci. 2021;14(11):5894. https://doi.org/10.1039/D1EE01739J.CrossRef

[132]

Li Z, Zhang Y, Zhang J, Cao Y, Chen J, Liu H, Wang Y. Sodium-ion battery with a wide operation-temperature range from −70 to 100 °C. Angewandte Chemie Int Edit. 2022;61(13):e202116930. https://doi.org/10.1002/anie.202116930.CrossRef

[133]

Wang N, Dong X, Wang B, Guo Z, Wang Z, Wang R, Qiu X, Wang Y. Zinc-organic battery with a wide operation-temperature window from −70 to 150 °C. Angew Chem Int Ed. 2020;59(34):14577. https://doi.org/10.1002/anie.202005603.CrossRef

[134]

Yue F, Tie Z, Deng S, Wang S, Yang M, Niu Z. An ultralow temperature aqueous battery with proton chemistry. Angew Chem Int Ed. 2021;60(25):13882. https://doi.org/10.1002/anie.202103722.CrossRef

[135]

Lu Y, Chen J. Prospects of organic electrode materials for practical lithium batteries. Nat Rev Chem. 2020;4(3):127. https://doi.org/10.1038/s41570-020-0160-9.CrossRef

[136]

Kirkpatrick J, McMorrow B, Turban DHP, Gaunt AL, Spencer JS, Matthews AGDG, Obika A, Thiry L, Fortunato M, Pfau D, Castellanos LR, Petersen S, Nelson AWR, Kohli P, Mori-Sánchez P, Hassabis D, Cohen AJ. Pushing the frontiers of density functionals by solving the fractional electron problem. Science. 2021;374(6573):1385. https://doi.org/10.1126/science.abj6511.CrossRef

[137]

Liu P, Verdi C, Karsai F, Kresse G. Phase transitions of zirconia: machine-learned force fields beyond density functional theory. Phys Rev B. 2022;105(6):L060102. https://doi.org/10.1103/PhysRevB.105.L060102.CrossRef

[138]

Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa JT, Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B. The fair guiding principles for scientific data management and stewardship. Sci Data. 2016;3(1):160018. https://doi.org/10.1038/sdata.2016.18.CrossRef

[139]

Draxl C, Scheffler M. Nomad: the fair concept for big data-driven materials science. MRS Bull. 2018;43(9):676. https://doi.org/10.1557/mrs.2018.208.CrossRef

[140]

Andersen CW, Armiento R, Blokhin E, Conduit GJ, Dwaraknath S, Evans ML, Fekete Á, Gopakumar A, Gražulis S, Merkys A, Mohamed F, Oses C, Pizzi G, Rignanese GM, Scheidgen M, Talirz L, Toher C, Winston D, Aversa R, Choudhary K, Colinet P, Curtarolo S, Di Stefano D, Draxl C, Er S, Esters M, Fornari M, Giantomassi M, Govoni M, Hautier G, Hegde V, Horton MK, Huck P, Huhs G, Hummelshøj J, Kariryaa A, Kozinsky B, Kumbhar S, Liu M, Marzari N, Morris AJ, Mostofi AA, Persson KA, Petretto G, Purcell T, Ricci F, Rose F, Scheffler M, Speckhard D, Uhrin M, Vaitkus A, Villars P, Waroquiers D, Wolverton C, Wu M, Yang X. Optimade, an api for exchanging materials data. Sci Data. 2021;8(1):217. https://doi.org/10.1038/s41597-021-00974-z.CrossRef

[141]

Wigh DS, Goodman JM, Lapkin AA. A review of molecular representation in the age of machine learning. WIREs Comput Mol Sci. 2022;12(5):e1603. https://doi.org/10.1002/wcms.1603.CrossRef

[142]

David L, Thakkar A, Mercado R, Engkvist O. Molecular representations in ai-driven drug discovery: a review and practical guide. J Chemin. 2020;12(1):56. https://doi.org/10.1186/s13321-020-00460-5.CrossRef

[143]

Liu B, Ramsundar B, Kawthekar P, Shi J, Gomes J, Luu Nguyen Q, Ho S, Sloane J, Wender P, Pande V. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent Sci. 2017;3(10):1103. https://doi.org/10.1021/acscentsci.7b00303.CrossRef

[144]

Coley CW, Green WH, Jensen KF. Machine learning in computer-aided synthesis planning. Acc Chem Res. 2018;51(5):1281. https://doi.org/10.1021/acs.accounts.8b00087.CrossRef

[145]

Dong J, Zhao M, Liu Y, Su Y, Zeng X. Deep learning in retrosynthesis planning: datasets, models and tools. Brief Bioinform. 2022;1:bbab391. https://doi.org/10.1093/bib/bbab391.CrossRef

[146]

Jorner K, Tomberg A, Bauer C, Sköld C, Norrby PO. Organic reactivity from mechanism to machine learning. Nat Rev Chem. 2021. https://doi.org/10.1038/s41570-021-00260-x.CrossRef

[147]

Pollock T, Allison J, Backman D, Boyce M, Gersh M, Holm E, LeSar R, Long M. Integrated Computational Materials Engineering: a Transformational Discipline for Improved Competitiveness and National Security. Washington DC: The National Acamedies Pres. 2008. 1.

[148]

Shen Y, Borowski JE, Hardy MA, Sarpong R, Doyle AG, Cernak T. Automation and computer-assisted planning for chemical synthesis. Nat Rev Methods Prim. 2021;1(1):23. https://doi.org/10.1038/s43586-021-00022-5.CrossRef

[149]

Mistry A, Franco AA, Cooper SJ, Roberts SA, Viswanathan V. How machine learning will revolutionize electrochemical sciences. ACS Energy Lett. 2021;6(4):1422. https://doi.org/10.1021/acsenergylett.1c00194.CrossRef

Title: Structural design of organic battery electrode materials: from DFT to artificial intelligence
Authors: Ting-Ting Wu
Gao-Le Dai
Jin-Jia Xu
Fang Cao
Xiao-Hong Zhang
Yu Zhao
Yu-Min Qian
Publication date: 28-08-2023
Publisher: Nonferrous Metals Society of China
Published in: Rare Metals / Issue 10/2023
Print ISSN: 1001-0521
Electronic ISSN: 1867-7185
DOI: https://doi.org/10.1007/s12598-023-02358-1

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