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

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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.

Graphical abstract

摘要

氧化还原活性有机材料因具有结构多样性和电化学性能可调等无机材料难以达到的优点, 正成为设计新型充电电池材料的新领域。更重要的是, 有机材料的可持续性开开发天然丰富的化学元素(C, H, N, O, S)使其成为锂离子电池的理想替代材料。然而, 在过去的几十年里, 合适的有机材料的鉴定和筛选仍然具有挑战性。本文在人工智能的辅助下, 综述了近二十年来氧化还原活性有机材料从DFT到机器学习等理论设计方面的研究进展, 重点介绍了预测化学/电化学稳定性和可逆性的计算方法。本文还将分析和讨论有机电池材料发展面临的挑战和前景。

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Reproduced with permission from Ref. [47]. Copyright 2019, Royal Society of Chemistry. b General funneling model for high-throughput computational screening computation screening of material design for organic battery (right); c computation complexity of various kinds of quantum simulation methods. d Schematic categorization of different ML algorithm. c and d were plotted according to Ref. [47]. Copyright 2019, Royal Society of Chemistry. Where NLDFT is non-local density-functional theory, QSDFT is quenched solid density-functional theory, CGDFT is coarse-grained classical density-functional theory, JDFT is joint density-functional theory, EDL is electrical double layer, GGA is generalized gradient approximation, WFT is wave-function theory

Fig. 5

Reproduced with permission from Ref. [53]. Copyright 2020, American Chemical Society

Fig. 6

Copyright 2018, Elsevier B.V. b Schematic illustration and DFT calculation of LUMO/HOMO state for various SR cathode products and corresponding ESP distribution. Reproduced with permission from Ref. [55], Copyright 2019, John Wiley & Sons, Inc. c HOMO state of selected molecules/anions with different extents of reduction. Reproduced with permission from Ref. [56]. Copyright 2013, Royal Society of Chemistry. d Redox mechanism of C6O6 and MESP of LinC6O6 (n = 0, 1, 2, 3, 4, 5, 6). Reproduced with permission from Ref. [57]. Copyright 2019, John Wiley & Sons, Inc

Fig. 7

Reproduced with permission from Ref. [62]. Copyright 2018, Elsevier B.V

Fig. 8

Copyright 2017, Royal Society of Chemistry. e DFT calculated LUMO level versus experimental redox potential of s-tetrazine series material. Modified with permission from Ref. [67]. Copyright 2019, John Wiley & Sons, Inc. f Linear dependence of average working potentials and calculated LUMO energies of phenanthraquinone (PQ), benzo[1,2-b:4,3-b’]dithiophene-4,5-quinone (BDTQ), and 1,10-phenanthroline-5,6-dione (PhenQ) (solid dots). Reproduced with permission from Ref. [56]. Copyright 2013, Royal Society of Chemistry. g Comparison of computed reduction (ΔE1red) and oxidation (ΔE1ox) potentials of selected anthraquinone derivatives. Reproduced with permission from Ref. [68]. Copyright 2016, American Chemical Society. h Correlations between redox potential and electron affinity for the quinone derivatives with or without a bound Li atom. Correlation is depicted by the arrow in gray. i Change in redox potentials as a function of number of electron-donating or electron-withdrawing groups. Reproduced with permission from Ref. [69]. Copyright 2016, American Chemical Society

Fig. 9

Reproduced with permission from Ref. [70]. Copyright 2020, American Chemical Society

Fig. 10

Reproduced with permission from Ref. [76]. Copyright 2022, American Chemical Society

Fig. 11

Reproduced with permission from Ref. [79]. Copyright 2020, John Wiley & Sons, Inc. b Chemical structure and redox mechanism of N,N′-substituted phenazine derivatives and π-LP-π bonding geometry showing N 2p-lone pair electron and π orbital of neighboring aromatic ring. Reproduced with permission from Ref. [19]. Copyright 2018, Elsevier B.V. c Optimized molecular structures of neutral and radical cation state with half-wave potentials (E1/2) and reorganization energies (λ) for their redox reactions for PhPTZ and PhPXZ and their electrochemical performance. Reproduced with permission from Ref. [82]. Copyright 2020, Royal Society of Chemistry. d Calculated NICS(1) values of BBD, ANQ, and PYD (upper) LUMO energy and redox properties of cyclic 1,2-diketones. Where Ered, Etol, and Ecoord are reduction energy, net energy, coordination energy, respectively. Reproduced with permission from Ref. [83]. Copyright 2012, American Chemical Society

Fig. 12

Reproduced with permission from Ref. [86], Copyright 2020, Springer Nature Limited. b Interaction between protonated urea and AADA3 calculated by DFT. ESP ranges are from –0.3 to 0.3 kcal·mol–1. c Molecular structures of different azobenzene (AZO)-based derivatives. d Solvation energy of different azobenzene-based derivatives calculated by DFT. Reproduced with permission from Ref. [87], Copyright 2015 John Wiley & Sons, Inc

Fig. 13

Reproduced with permission from Ref. [88]. Copyright 2017, American Chemical Society. b Schematic diagram of two-step sodiation and desodiation process of TQBQ-COF electrode obtained via calculated molecular electrostatic potential (MESP) method based on optimized structure. Reproduced with permission from Ref. [90]. Copyright 2020, Springer Nature Limited. c Reaction sited assigning by calculated MESP and biredox mechanism of CuPcNA-CMP with anion (PF6–) or cation (Li+) during charge/discharge process. Reproduced with permission from Ref. [91]. Copyright 2021, John Wiley & Sons, Inc

Fig. 14

Reproduced with permission from Ref. [93]. Copyright 2014, Royal Society of Chemistry. b Charge density difference isosurfaces of BQ/graphene, BQ-2Oli/graphene and BQ-2COOLi/graphene. Reproduced with permission from Ref. [94]. Copyright 2017, Springer Nature. c Schematic process for preparing NQS/MWNTs hybrid films and optical photographs of a NQS/MWNTs hybrid film. Reproduced with permission from Ref. [95]. Copyright 2017, American Chemical Society

Fig. 15

Reproduced with permission from Ref. [31]. Copyright 2010 Elsevier B.V. c Process of conversion of waste PET flakes to disodium terephthalate with Li- and Na-ion storage. Reproduced with permission from Ref. [97]. Copyright 2020, American Chemical Society

Fig. 16

Reproduced with permission from Ref. [100], Copyright 2019, John Wiley & Sons, Inc. b Snapshots of MD simulations of BQ-LiTFSI, Li-TEMPO, Na-TEMPO, K-TEMPO and Li-MeOTEMPO organic eutectic electrolyte (OEE) structure and their corresponding vdW interaction. Reproduced with permission from Ref. [92], Copyright 2021, John Wiley & Sons, Inc. MD simulation of Na+ diffusion mechanism with Na+ trajectories (small purple bullets) of in Na2C6H2O4 (upper panel) and Na4C6H2O4 (lower panel) at 1200 K for 15 ps. Reproduced with permission from Ref. [101]. Copyright 2015, American Association for the Advancement of Science. c Relative molecule geometry and d distribution of Li–O binding geometry at different concentration by calculation redial distribution function from molecular dynamics. Four electrolyte solutions with LiDFOB (LFB) concentrations of 1.0, 1.5, 2.0, and 2.5 mol·L–1 were prepared and denoted as LFB1.0, LFB1.5, LFB2.0, and LFB2.5, respectively. Reproduced with permission from Ref. [102], Copyright 2021, Elsevier B.V

Fig. 17

Reproduced with permission from Ref. [106]. Copyright 2019, Royal Society of Chemistry. b Comparison of calculated and experimental potentials of nine reported organic materials with different methods. Reproduced with permission from Ref. [107]. Copyright 2015, American Chemical Society. c High-throughput computations screening protocol for candidate molecules for electrical energy storage applications. Reproduced with permission from Ref. [108]. Copyright 2015, AIP Publishing. d Molecular property prediction flowchart by machine learning models. RNN, MW, PSA and lgP are recurrent neural networks, molecular weight, polar surface area and lipophilic properties of compounds, respectively. Reproduced with permission from Ref. [110]. Copyright 2019, Elsevier B.V

Fig. 18

Reproduced with permission from Ref. [111]. Copyright 2020, Royal Society of Chemistry

Fig. 19

Reproduced with permission from Ref. [113]. Copyright 2014, the Royal Society of Chemistry

Fig. 20

Reproduced with permission from Ref. [28]. Copyright 2019, Royal Society of Chemistry

Fig. 21

Reproduced with permission from Ref. [117]. Copyright 2021, American Chemical Society. b GP-calibrated reduction potential versus expected electronic coupling for top-10% lowest reduction potential molecules. Reproduced with permission from Ref. [118]. Copyright 2019, Royal Society of Chemistry. c A simplified representation of neural network model and neural model performance in test dataset of 2290 molecules in predicting reduction potentials. Reproduced with permission from Ref. [119]. Copyright 2022, Elsevier B.V

Fig. 22

Reproduced with permission from Ref. [121]. Copyright 2019, Royal Society of Chemistry

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Acknowledgements

This work is financially supported by the National Natural Science Foundation of China (Nos. 22179031 and 22109111), the Natural Science Foundation of Zhejiang Province (Nos. LY22B030008 and LQ22B030006), the Collaborative Innovation Center of Suzhou Nano Science & Technology, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 Project, the Open Research Fund of CNMGE Platform & NSCC-TJ (No. CNGME202101006) and the support from Hefei advanced computing center.

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Authors and Affiliations

  1. Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Soochow University, Suzhou, 215123, China

    Ting-Ting Wu, Xiao-Hong Zhang & Yu-Min Qian

  2. College of Materials, Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou, 311121, China

    Gao-Le Dai & Yu Zhao

  3. Department of Chemistry and Biochemistry, University of Missouri–St. Louis, St. Louis, MO, 63121, USA

    Jin-Jia Xu

  4. Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark

    Fang Cao

  5. Key Lab of Advanced Optoelectronic Quantum Architecture and Measurement, Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems, and School of Physics, Beijing Institute of Technology, Beijing, 100081, China

    Yu-Min Qian

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  1. Ting-Ting Wu
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Correspondence to Yu Zhao or Yu-Min Qian.

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Wu, TT., Dai, GL., Xu, JJ. et al. Structural design of organic battery electrode materials: from DFT to artificial intelligence. Rare Met. 42, 3269–3303 (2023). https://doi.org/10.1007/s12598-023-02358-1

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  • DOI: https://doi.org/10.1007/s12598-023-02358-1

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