Journal of the Chinese Ceramic Society, Volume. 53, Issue 7, 1801(2025)

Intelligent Model for High Throughput Screening of Inorganic Sodium Solid-State Electrolytes

LIU Yihong, BI Wenzhu, Tamerd Mohamed Ait, and YANG Menghao
Author Affiliations
  • Shanghai Key Laboratory for R&D and Application of Metallic Functional Materials, Institute of New Energy for Vehicles, School of Materials Science and Engineering, Tongji University, Shanghai 201804, China
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    References(36)

    [1] [1] KIM S W, SEO D H, MA X H, et al. Electrode materials for rechargeable sodium-ion batteries: Potential alternatives to current lithium-ion batteries[J]. Adv Energy Mater, 2012, 2(7): 710–721.

    [2] [2] KIM J J, YOON K, PARK I, et al. Progress in the development of sodium-ion solid electrolytes[J]. Small Meth, 2017, 1(10): 1700219.

    [3] [3] ZHAO C L, LIU L L, QI X G, et al. Solid-state sodium batteries[J]. Adv Energy Mater, 2018, 8(17): 1703012.

    [4] [4] AHMAD H, KUBRA K T, BUTT A, et al. Recent progress, challenges, and perspectives in the development of solid-state electrolytes for sodium batteries[J]. J Power Sources, 2023, 581: 233518.

    [5] [5] GOODENOUGH J B, HONG H Y, KAFALAS J A. Fast Na+-ion transport in skeleton structures[J]. Mater Res Bull, 1976, 11(2): 203–220.

    [6] [6] LI Z P, LIU P, ZHU K J, et al. Solid-state electrolytes for sodium metal batteries[J]. Energy Fuels, 2021, 35(11): 9063–9079.

    [7] [7] KIM J, KANG S, MIN K. Screening platform for promising Na superionic conductors for Na-ion solid-state electrolytes[J]. ACS Appl Mater Interfaces, 2023, 15(35): 41417–41425.

    [8] [8] ZHANG Y, ZHAN T, SUN Y, et al. Revolutionizing solid-state NASICON sodium batteries: Enhanced ionic conductivity estimation through multivariate experimental parameters leveraging machine learning[J]. ChemSusChem, 2024, 17(6): e202301284.

    [9] [9] ZHOU P F, ZHAO Z R, SUN K T, et al. Machine learning guided cobalt-doping strategy for solid-state NASICON electrolytes[J]. Eur J Inorg Chem, 2023, 26(26): e202300382.

    [10] [10] LEE B D, GAVALI D S, KIM H, et al. Discovering virtual Na-based argyrodites as solid-state electrolytes using DFT, AIMD, and machine learning techniques[J]. J Mater Chem A, 2025, https://doi.org/10.1039/D4TA06927G.

    [11] [11] GUIN M, TIETZ F, GUILLON O. New promising NASICON material as solid electrolyte for sodium-ion batteries: Correlation between composition, crystal structure and ionic conductivity of Na3+xSc2SixP3−xO12[J]. Solid State Ion, 2016, 293: 18–26.

    [12] [12] DENG Z, ZHU Z Y, CHU I H, et al. Data-driven first-principles methods for the study and design of alkali superionic conductors[J]. Chem Mater, 2017, 29(1): 281–288.

    [13] [13] XIAO W S, WU M W, WANG H, et al. Li-ion transport mechanisms in selenide-based solid-state electrolytes in lithium-metal batteries: A study of Li8SeN2, Li7PSe6, and Li6PSe5X (X = Cl, Br, I)[J]. Energy Environ Mater, 2024, 7(5): e12729.

    [14] [14] JAIN A, ONG S P, HAUTIER G, et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation[J]. 2013, 1(1): 011002.

    [15] [15] HONRAO S J, YANG X, RADHAKRISHNAN B, et al. Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening[J]. Sci Rep, 2021, 11(1): 16484.

    [16] [16] WARD L, AGRAWAL A, CHOUDHARY A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials[J]. NPJ Comput Mater, 2016, 2: 16028.

    [17] [17] BREIMAN L. Random forests[J]. Mach Learn, 2001, 45(1): 5–32.

    [18] [18] CHEN T Q, GUESTRIN C. XGBoost: A scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California USA. ACM, 2016.

    [19] [19] Prokhorenkova L, GUSEV G, VOROBEV A, et al. CatBoost: unbiased boosting with categorical features[C]//32nd Conference on Neural Information Processing Systems (NeurIPS 2018). Montral, Canada, 2018: 31.

    [20] [20] COVER T, HART P. Nearest neighbor pattern classification[J]. IEEE Trans Inf Theory, 1967, 13(1): 21–27.

    [21] [21] PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al. Scikit-learn: Machine learning in python [J]. J. Mach Learn Res, 2011, 12: 2825–2830.

    [22] [22] CHEN H H, CHEN J P, DING J H. Data evaluation and enhancement for quality improvement of machine learning[J]. IEEE Trans Reliab, 2021, 70(2): 831–847.

    [23] [23] LIU Y, YANG Z, ZOU X, et al. Data quantity governance for machine learning in materials science[J]. Natl Sci Rev, 2023, 10(7): nwad125.

    [24] [24] YANG F L, CAMPOS DOS SANTOS E, JIA X, et al. A dynamic database of solid-state electrolyte (DDSE) picturing all-solid-state batteries[J]. Nano Mater Sci, 2024, 6(2): 256–262.

    [25] [25] KRESSE G, FURTHMLLER J. Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set[J]. Phys Rev B Condens Matter, 1996, 54(16): 11169–11186.

    [26] [26] KRESSE G, FURTHMLLER J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set[J]. Comput Mater Sci, 1996, 6(1): 15–50.

    [27] [27] BLCHL P E. Projector augmented-wave method[J]. Phys Rev B, 1994, 50(24): 17953–17979.

    [28] [28] PERDEW J P, BURKE K, ERNZERHOF M. Generalized gradient approximation made simple[J]. Phys Rev Lett, 1996, 77(18): 3865–3868.

    [29] [29] HE X F, ZHU Y Z, EPSTEIN A, et al. Statistical variances of diffusional properties fromab initiomolecular dynamics simulations[J]. NPJ Comput Mater, 2018, 4: 18.

    [30] [30] HOOVER W G. Canonical dynamics: Equilibrium phase-space distributions[J]. Phys Rev A, 1985, 31(3): 1695–1697.

    [31] [31] NOS S. A unified formulation of the constant temperature molecular dynamics methods[J]. 1984, 81(1): 511–519.

    [32] [32] MOMMA K, IZUMI F.VESTA3for three-dimensional visualization of crystal, volumetric and morphology data[J]. J Appl Crystallogr, 2011, 44(6): 1272–1276.

    [33] [33] HENKELMAN G, UBERUAGA B P, JNSSON H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths[J]. 2000, 113(22): 9901–9904.

    [34] [34] OUYANG B, WANG J, HE T, et al. Synthetic accessibility and stability rules of NASICONs[J]. Nat Commun, 2021, 12(1): 5752.

    [35] [35] ZHANG Z Z, ZOU Z Y, KAUP K, et al. Correlated migration invokes higher Na+-ion conductivity in NaSICON-type solid electrolytes[J]. Adv Energy Mater, 2019, 9(42): 1902373.

    [36] [36] LI C, LI R, LIU K N, et al. NaSICON: A promising solid electrolyte for solid-state sodium batteries[J]. Interdiscip Mater, 2022, 1(3): 396–416.

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    LIU Yihong, BI Wenzhu, Tamerd Mohamed Ait, YANG Menghao. Intelligent Model for High Throughput Screening of Inorganic Sodium Solid-State Electrolytes[J]. Journal of the Chinese Ceramic Society, 2025, 53(7): 1801

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    Paper Information

    Special Issue:

    Received: Dec. 11, 2024

    Accepted: Aug. 12, 2025

    Published Online: Aug. 12, 2025

    The Author Email:

    DOI:10.14062/j.issn.0454-5648.20240787

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