[1] [1] HAN X, CHEN J Z, CHEN M F, et al. Induction of planar Li growth with designed interphases for dendrite-free Li metal anodes[J]. Energy Storage Mater, 2021, 39: 250–258.

[2] [2] CHENG X B, ZHANG R, ZHAO C Z, et al. Toward safe lithium metal anode in rechargeable batteries: A review[J]. Chem Rev, 2017, 117(15): 10403–10473.

[3] [3] JANEK J, ZEIER W G. A solid future for battery development[J]. Nat Energy, 2016, 1(9): 16141.

[4] [4] LI S M, CHEN Z F, ZHANG W T, et al. High-throughput screening of protective layers to stabilize the electrolyte-anode interface in solid-state Li-metal batteries[J]. Nano Energy, 2022, 102: 107640.

[5] [5] GOODENOUGH J B. Electrochemical energy storage in a sustainable modern society[J]. Energy Environ Sci, 2014, 7(1): 14–18.

[6] [6] WANG S, FU J M, LIU Y S, et al. Design principles for sodium superionic conductors[J]. Nat Commun, 2023, 14(1): 7615.

[7] [7] ZHU Y Z, HE X F, MO Y F. Origin of outstanding stability in the lithium solid electrolyte materials: Insights from thermodynamic analyses based on first-principles calculations[J]. ACS Appl Mater Interfaces, 2015, 7(42): 23685–23693.

[8] [8] HE X F, ZHU Y Z, MO Y F. Origin of fast ion diffusion in super-ionic conductors[J]. Nat Commun, 2017, 8: 15893.

[9] [9] ZHANG B K, ZHONG J J, ZHANG Y P, et al. Discovering a New class of fluoride solid-electrolyte materialsviascreening the structural property of Li-ion sublattice[J]. Nano Energy, 2021, 79: 105407.

[10] [10] JUN K, SUN Y Z, XIAO Y H, et al. Lithium superionic conductors with corner-sharing frameworks[J]. Nat Mater, 2022, 21: 924–931.

[11] [11] XU J, WANG Y Q, WU S Y, et al. New halide-based sodium-ion conductors Na3Y2Cl9 inversely designed by building block construction[J]. ACS Appl Mater Interfaces, 2023, 15(17): 21086–21096.

[12] [12] LAVRINENKO A K, FAMPRIKIS T, QUIRK J A, et al. Optimizing ionic transport in argyrodites: A unified view on the role of sulfur/halide distribution and local environments[J]. J Mater Chem A Mater, 2024, 12(39): 26596–26611.

[13] [13] ZENG Y, OUYANG B, LIU J, et al. High-entropy mechanism to boost ionic conductivity[J]. Science, 2022, 378(6626): 1320–1324.

[14] [14] JANG S H, TATEYAMA Y, JALEM R. High-throughput data-driven prediction of stable high-performance Na-ion sulfide solid electrolytes[J]. Adv Funct Mater, 2022, 32(48): 2206036.

[15] [15] SENDEK A D, YANG Q, CUBUK E D, et al. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials[J]. Energy Environ Sci, 2017, 10(1): 306–320.

[16] [16] FU X, WANG Y Q, XU J, et al. First-principles study on a new chloride solid lithium-ion conductor material with high ionic conductivity[J]. J Mater Chem A, 2024, 12(17): 10562–10570.

[17] [17] LI Y, YU J H. Emerging applications of zeolites in catalysis, separation and host–guest assembly[J]. Nat Rev Mater, 2021, 6: 1156–1174.

[18] [18] KELEMEN G, SCHN G. Ionic conductivity in dehydrated zeolites[J]. J Mater Sci, 1992, 27(22): 6036–6040.

[19] [19] LI M L, CHI X W, YU J H. Zeolite-based electrolytes: A promising choice for solid-state batteries[J]. PRX Energy, 2022, 1(3): 031001.

[20] [20] CHI X W, LI M L, DI J C, et al. A highly stable and flexible zeolite electrolyte solid-state Li-air battery[J]. Nature, 2021, 592(7855): 551–557.

[21] [21] PLUTH J J, SMITH J V. Accurate redetermination of crystal structure of dehydrated zeolite A. Absence of near zero coordination of sodium. Refinement of silicon, aluminum-ordered superstructure[J]. J Am Chem Soc, 1980, 102(14): 4704–4708.

[22] [22] ANSTINE D M, ISAYEV O. Machine learning interatomic potentials and long-range physics[J]. J Phys Chem A, 2023, 127(11): 2417–2431.

[23] [23] LIU Y, GUO B R, ZOU X X, et al. Machine learning assisted materials design and discovery for rechargeable batteries[J]. Energy Storage Mater, 2020, 31: 434–450.

[24] [24] DERINGER V L, CARO M A, CSNYI G. Machine learning interatomic potentials as emerging tools for materials science[J]. Adv Mater, 2019, 31(46): e1902765.

[25] [25] LEE J, JU S, HWANG S, et al. Disorder-dependent Li diffusion in Li6PS5Cl investigated by machine-learning potential[J]. ACS Appl Mater Interfaces, 2024, 16(35): 46442–46453.

[26] [26] LIU Y S, HE X F, MO Y F. Discrepancies and error evaluation metrics for machine learning interatomic potentials[J]. NPJ Comput Mater, 2023, 9: 174.

[27] [27] HIMANEN L, JGER M O J, MOROOKA E V, et al. DScribe: Library of descriptors for machine learning in materials science[J]. Comput Phys Commun, 2020, 247: 106949.

[28] [28] DE S, BARTK A P, CSNYI G, et al. Comparing molecules and solids across structural and alchemical space[J]. Phys Chem Chem Phys, 2016, 18(20): 13754–13769.

[29] [29] 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.

[30] [30] BLCHL P E. Projector augmented-wave method[J]. Phys Rev B, 1994, 50(24): 17953–17979.

[31] [31] PERDEW J P, ERNZERHOF M, BURKE K. Rationale for mixing exact exchange with density functional approximations[J]. J Chem Phys, 1996, 105(22): 9982–9985.

[32] [32] PERDEW J P, BURKE K, ERNZERHOF M. Generalized gradient approximation made simple[J]. Phys Rev Lett, 1996, 77(18): 3865–3868.

[33] [33] NOS S. Constant temperature molecular dynamics methods[J]. Prog Theor Phys Suppl, 1991, 103: 1–46.

[34] [34] DENG B W, ZHONG P C, JUN K, et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling[J]. Nat Mach Intell, 2023, 5(9): 1031–1041.

[35] [35] LARSEN A H, MORTENSEN J J, BLOMQVIST J, et al. The atomic simulation environment-a Python library for working with atoms[J]. J Phys Condens Matter, 2017, 29(27): 273002.

[36] [36] 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.