Progress of theoretical studies on the formation and growth mechanisms of solid electrolyte interphase at lithium metal anodes

doi:10.19799/j.cnki.2095-4239.2024.0586

Abstract

Abstract:

Lithium metal anodes in lithium-ion batteries (LIBs) have garnered significant research interest due to their exceptionally high theoretical specific capacity. However, their high reactivity can trigger a series of complex degradation reactions of electrolyte components, ultimately forming a solid electrolyte interface (SEI) on the electrode surfaces. This SEI passivation layer suppresses the continuous loss of electrolytes but can significantly affect the cycling performance of the LIBs. Consequently, understanding the formation and growth mechanisms of SEI at the atomic/molecular level has become a major research focus in recent years. This study summarizes the latest research progress on the structure, composition, and growth process of SEI using various theoretical approaches. Particularly, it introduces some successful examples of classical molecular dynamics, reactive force field molecular dynamics, first-principles molecular dynamics, machine learning force field molecular dynamics, and kinetic Monte Carlo methods in SEI modeling. In addition, this study discusses the limitations of current theoretical approaches in simulating the formation and growth mechanisms of SEI. Finally, this study proposes that combining machine learning methods and kinetic Monte Carlo approaches to enhance the simulations of the formation and growth processes of SEI over long-time domains.

Key words: solid electrolyte interphase, growth and formation mechanism, molecular dynamics, kinetics Monte Carlo, machine learning

CLC Number:

TM 91

Guobing ZHOU, Shenzhen XU. Progress of theoretical studies on the formation and growth mechanisms of solid electrolyte interphase at lithium metal anodes[J]. Energy Storage Science and Technology, 2024, 13(9): 3150-3160.

Figures/Tables 7

Table 1

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References 43

1	MASIAS A, MARCICKI J, PAXTON W A. Opportunities and challenges of lithium ion batteries in automotive applications[J]. ACS Energy Letters, 2021, 6(2): 621-630. DOI: 10.1021/acsenergylett.0c02584.
2	KIM S, PARK G, LEE S J, et al. Lithium-metal batteries: From fundamental research to industrialization[J]. Advanced Materials, 2023, 35(43): e2206625. DOI: 10.1002/adma.202206625.
3	KWAK W J, ROSY, SHARON D, et al. Lithium-oxygen batteries and related systems: Potential, status, and future[J]. Chemical Reviews, 2020, 120(14): 6626-6683. DOI: 10.1021/acs.chemrev.9b00609.
4	ZHANG X, YANG Y A, ZHOU Z. Towards practical lithium-metal anodes[J]. Chemical Society Reviews, 2020, 49(10): 3040-3071. DOI: 10.1039/c9cs00838a.
5	LIU D H, BAI Z Y, LI M, et al. Developing high safety Li-metal anodes for future high-energy Li-metal batteries: Strategies and perspectives[J]. Chemical Society Reviews, 2020, 49(15): 5407-5445. DOI: 10.1039/c9cs00636b.
6	MENG X Q, XU Y L, CAO H B, et al. Internal failure of anode materials for lithium batteries—A critical review[J]. Green Energy & Environment, 2020, 5(1): 22-36. DOI: 10.1016/j.gee. 2019.10.003.
7	YU X W, MANTHIRAM A. Electrode-electrolyte interfaces in lithium-based batteries[J]. Energy & Environmental Science, 2018, 11(3): 527-543. DOI: 10.1039/C7EE02555F.
8	ADENUSI H, CHASS G A, PASSERINI S, et al. Lithium batteries and the solid electrolyte interphase (SEI)—Progress and outlook[J]. Advanced Energy Materials, 2023, 13(10): 2203307. DOI: 10.1002/aenm.202203307.
9	GAUTHIER M, CARNEY T J, GRIMAUD A, et al. Electrode-electrolyte interface in Li-ion batteries: Current understanding and new insights[J]. The Journal of Physical Chemistry Letters, 2015, 6(22): 4653-4672. DOI: 10.1021/acs.jpclett.5b01727.
10	MENKIN S, GOLODNITSKY D, PELED E. Artificial solid-electrolyte interphase (SEI) for improved cycleability and safety of lithium-ion cells for EV applications[J]. Electrochemistry Communications, 2009, 11(9): 1789-1791. DOI: 10.1016/j.elecom.2009.07.019.
11	SHIM J, KOSTECKI R, RICHARDSON T, et al. Electrochemical analysis for cycle performance and capacity fading of a lithium-ion battery cycled at elevated temperature[J]. Journal of Power Sources, 2002, 112(1): 222-230. DOI: 10.1016/S0378-7753(02)00363-4.
12	SMITH A J, BURNS J C, TRUSSLER S, et al. Precision measurements of the coulombic efficiency of lithium-ion batteries and of electrode materials for lithium-ion batteries[J]. Journal of the Electrochemical Society, 2010, 157(2): A196. DOI: 10.1149/1.3268129.
13	SHAN X Y, ZHONG Y, ZHANG L J, et al. A brief review on solid electrolyte interphase composition characterization technology for lithium metal batteries: Challenges and perspectives[J]. The Journal of Physical Chemistry C, 2021, 125(35): 19060-19080. DOI: 10.1021/acs.jpcc.1c06277.
14	WU J X, IHSAN-UL-HAQ M, CHEN Y M, et al. Understanding solid electrolyte interphases: Advanced characterization techniques and theoretical simulations[J]. Nano Energy, 2021, 89: 106489. DOI: 10.1016/j.nanoen.2021.106489.
15	VERMA P, MAIRE P, NOVÁK P. A review of the features and analyses of the solid electrolyte interphase in Li-ion batteries[J]. Electrochimica Acta, 2010, 55(22): 6332-6341. DOI: 10.1016/j.electacta.2010.05.072.
16	WU H P, JIA H, WANG C M, et al. Recent progress in understanding solid electrolyte interphase on lithium metal anodes[J]. Advanced Energy Materials, 2021, 11(5): 2003092. DOI: 10.1002/aenm.202003092.
17	张慧敏, 王京, 王一博, 等. 锂离子电池SEI多尺度建模研究展望[J]. 储能科学与技术, 2023, 12(2): 366-382. DOI: 10.19799/j.cnki.2095-4239.2022.0504.
	ZHANG H M, WANG J, WANG Y B, et al. Multiscale modeling of the SEI of lithium-ion batteries[J]. Energy Storage Science and Technology, 2023, 12(2): 366-382. DOI: 10.19799/j.cnki.2095-4239.2022.0504.
18	于沛平, 许亮, 麻冰云, 等. 多尺度模拟研究固体电解质界面[J]. 储能科学与技术, 2022, 11(3): 921-928. DOI: 10.19799/j.cnki.2095-4239.2022.0046.
	YU P P, XU L, MA B Y, et al. Multiscale simulation of a solid electrolyte interphase[J]. Energy Storage Science and Technology, 2022, 11(3): 921-928. DOI: 10.19799/j.cnki.2095-4239.2022.0046.
19	TAKENAKA N, BOUIBES A, YAMADA Y, et al. Frontiers in theoretical analysis of solid electrolyte interphase formation mechanism[J]. Advanced Materials, 2021, 33(37): e2100574. DOI: 10.1002/adma.202100574.
20	WU Q S, MCDOWELL M T, QI Y. Effect of the electric double layer (EDL) in multicomponent electrolyte reduction and solid electrolyte interphase (SEI) formation in lithium batteries[J]. Journal of the American Chemical Society, 2023, 145(4): 2473-2484. DOI: 10.1021/jacs.2c11807.
21	JORN R, KUMAR R, ABRAHAM D P, et al. Atomistic modeling of the electrode-electrolyte interface in Li-ion energy storage systems: Electrolyte structuring[J]. The Journal of Physical Chemistry C, 2013, 117(8): 3747-3761. DOI: 10.1021/jp3102282.
22	KANG P B, CHEN D L, WU L Y, et al. Insight into poly(1,3-dioxolane)-based polymer electrolytes and their interfaces with lithium metal: Effect of electrolyte compositions[J]. Chemical Engineering Journal, 2023, 455: 140931. DOI: 10.1016/j.cej.2022.140931.
23	LOURENÇO T C, EBADI M, BRANDELL D, et al. Interfacial structures in ionic liquid-based ternary electrolytes for lithium-metal batteries: A molecular dynamics study[J]. The Journal of Physical Chemistry B, 2020, 124(43): 9648-9657. DOI: 10.1021/acs.jpcb.0c06500.
24	VAN DUIN A C T, DASGUPTA S, LORANT F, et al. ReaxFF: A reactive force field for hydrocarbons[J]. The Journal of Physical Chemistry A, 2001, 105(41): 9396-9409. DOI: 10.1021/jp004368u.
25	LEE H G, KIM S Y, LEE J S. Dynamic observation of dendrite growth on lithium metal anode during battery charging/discharging cycles[J]. NPJ Computational Materials, 2022, 8: 103. DOI: 10.1038/s41524-022-00788-6.
26	YANG P Y, PAO C W. Molecular simulations of the microstructure evolution of solid electrolyte interphase during cyclic charging/discharging[J]. ACS Applied Materials & Interfaces, 2021, 13(4): 5017-5027. DOI: 10.1021/acsami.0c18783.
27	XIE M, WU Y, LIU Y, et al. Pathway of in situ polymerization of 1,3-dioxolane in LiPF₆ electrolyte on Li metal anode[J]. Materials Today Energy, 2021, 21: 100730. DOI: 10.1016/j.mtener. 2021.100730.
28	LIU Y, SUN Q T, YU P P, et al. Effects of high and low salt concentrations in electrolytes at lithium-metal anode surfaces using DFT-ReaxFF hybrid molecular dynamics method[J]. The Journal of Physical Chemistry Letters, 2021, 12(11): 2922-2929. DOI: 10.1021/acs.jpclett.1c00279.
29	YU P P, SUN Q T, LIU Y, et al. Multiscale simulation of solid electrolyte interface formation in fluorinated diluted electrolytes with lithium anodes[J]. ACS Applied Materials & Interfaces, 2022, 14(6): 7972-7979. DOI: 10.1021/acsami.1c22610.
30	YANG M Y, ZYBIN S V, DAS T, et al. Characterization of the solid electrolyte interphase at the Li metal-ionic liquid interface[J]. Advanced Energy Materials, 2023, 13(3): 2202949. DOI: 10.1002/aenm.202202949.
31	MERINOV B V, ZYBIN S V, NASERIFAR S, et al. Interface structure in Li-metal/[Pyr₁₄][TFSI]-ionic liquid system from ab initio molecular dynamics simulations[J]. The Journal of Physical Chemistry Letters, 2019, 10(16): 4577-4586. DOI: 10.1021/acs.jpclett.9b01515.
32	DHATTARWAL H S, CHEN Y W, KUO J L, et al. Mechanistic insight on the formation of a solid electrolyte interphase (SEI) by an acetonitrile-based superconcentrated [Li][TFSI] electrolyte near lithium metal[J]. The Journal of Physical Chemistry C, 2020, 124(50): 27495-27502. DOI: 10.1021/acs.jpcc.0c08009.
33	GALVEZ-ARANDA D E, SEMINARIO J M. Li-metal anode in dilute electrolyte LiFSI/TMP: Electrochemical stability using ab initio molecular dynamics[J]. The Journal of Physical Chemistry C, 2020, 124(40): 21919-21934. DOI: 10.1021/acs.jpcc.0c04240.
34	ZHENG Y, SOTO F A, PONCE V, et al. Localized high concentration electrolyte behavior near a lithium-metal anode surface[J]. Journal of Materials Chemistry A, 2019, 7(43): 25047-25055. DOI: 10.1039/C9TA08935G.
35	HU T P, TIAN J X, DAI F Z, et al. Impact of the local environment on Li ion transport in inorganic components of solid electrolyte interphases[J]. Journal of the American Chemical Society, 2023, 145(2): 1327-1333. DOI: 10.1021/jacs.2c11521.
36	FU F J, WANG X X, ZHANG L F, et al. Unraveling the atomic-scale mechanism of phase transformations and structural evolutions during (de)lithiation in Si anodes[J]. Advanced Functional Materials, 2023, 33(37): 2303936. DOI: 10.1002/adfm.202303936.
37	REN F C, WU Y Q, ZUO W H, et al. Visualizing the SEI formation between lithium metal and solid-state electrolyte[J]. Energy & Environmental Science, 2024, 17(8): 2743-2752. DOI: 10.1039/D3EE03536K.
38	FANG F, LIN J, LI J J, et al. Molecular dynamics simulations of liquid gallium alloy Ga-X (X = Pt, Pd, Rh) via machine learning potentials[J]. Inorganic Chemistry Frontiers, 2024, 11(5): 1573-1582. DOI: 10.1039/D3QI02410E.
39	XU L K, SHAO W, JIN H S, et al. Data efficient and stability indicated sampling for developing reactive machine learning potential to achieve ultralong simulation in lithium-metal batteries[J]. The Journal of Physical Chemistry C, 2023, 127(50): 24106-24117. DOI: 10.1021/acs.jpcc.3c05522.
40	ESMAEILPOUR M, JANA S, LI H J, et al. A solution-mediated pathway for the growth of the solid electrolyte interphase in lithium-ion batteries[J]. Advanced Energy Materials, 2023, 13(14): 2203966. DOI: 10.1002/aenm.202203966.
41	SITAPURE N, LEE H, OSPINA-ACEVEDO F, et al. A computational approach to characterize formation of a passivation layer in lithium metal anodes[J]. AIChE Journal, 2021, 67(1): e17073. DOI: 10.1002/aic.17073.
42	GERASIMOV M, SOTO F A, WAGNER J, et al. Species distribution during solid electrolyte interphase formation on lithium using MD/DFT-parameterized kinetic Monte Carlo simulations[J]. The Journal of Physical Chemistry C, 2023, 127(10): 4872-4886. DOI: 10.1021/acs.jpcc.2c05898.
43	WAGNER-HENKE J, KUAI D C, GERASIMOV M, et al. Knowledge-driven design of solid-electrolyte interphases on lithium metal via multiscale modelling[J]. Nature Communications, 2023, 14(1): 6823. DOI: 10.1038/s41467-023-42212-7.

Simulation methods	Time/Spatial scale	Features
CMD	ns/nm	Advantages: high computational efficiency Disadvantages: accuracy limit, not suitable for chemical reactions
RxMD	ns/nm	Advantages: capable of modeling chemical reactions Disadvantages: accuracy limit, complex potential development
AIMD	ps/nm	Advantages: high accuracy, capable of modeling chemical reactions Disadvantages: high computational cost, limited system size
MLMD	ns/nm	Advantages: high computational efficiency, high accuracy, capable of modeling chemical reactions Disadvantages: transferability limitations
KMC	s/nm~μm	Advantages: long timescales, efficient sampling Disadvantages: event list requirement

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