[1] [1] YAO N, CHEN X, FU Z H, et al. Applying classical, ab initio, and machine-learning molecular dynamics simulations to the liquid electrolyte for rechargeable batteries[J]. Chem Rev, 2022, 122(12): 10970-11021.

[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] KWADE A, HASELRIEDER W, LEITHOFF R, et al. Current status and challenges for automotive battery production technologies[J]. Nat Energy, 2018, 3(4): 290-300.

[4] [4] LARCHER D, TARASCON J M. Towards greener and more sustainable batteries for electrical energy storage[J]. Nat Chem, 2015, 7(1): 19-29.

[5] [5] ARMAND M, TARASCON J M. Building better batteries[J]. Nature, 2008, 451(7179): 652-657.

[6] [6] MANTHIRAM A. An outlook on lithium ion battery technology[J]. ACS Cent Sci, 2017, 3(10): 1063-1069.

[7] [7] MASIAS A, MARCICKI J, PAXTON W A. Opportunities and challenges of lithium ion batteries in automotive applications[J]. ACS Energy Lett, 2021, 6(2): 621-630.

[8] [8] ZENG L, QIU L, CHENG H M. Towards the practical use of flexible lithium ion batteries[J]. Energy Storage Mater, 2019, 23: 434-438.

[9] [9] ALBERTUS P, BABINEC S, LITZELMAN S, et al. Status and challenges in enabling the lithium metal electrode for high-energy and low-cost rechargeable batteries[J]. Nat Energy, 2017, 3(1): 16-21.

[10] [10] LIU J, BAO Z, CUI Y, et al. Pathways for practical high-energy long-cycling lithium metal batteries[J]. Nat Energy, 2019, 4(3): 180-186.

[11] [11] CHEN X, HOU T Z, LI B, et al. Towards stable lithium-sulfur batteries: Mechanistic insights into electrolyte decomposition on lithium metal anode[J]. Energy Storage Mater, 2017, 8: 194-201.

[12] [12] SHEN X, LIU H, CHENG X B, et al. Beyond lithium ion batteries: Higher energy density battery systems based on lithium metal anodes[J]. Energy Storage Mater, 2018, 12: 161-175.

[13] [13] FU Z H, CHEN X, ZHANG Q. Review on the lithium transport mechanism in solid-state battery materials[J]. WIREs Comput Mol Sci, 2022, DOI: 10.1002/wcms.1621.

[14] [14] BANERJEE A, WANG X, FANG C, et al. Interfaces and interphases in all-solid-state batteries with inorganic solid electrolytes[J]. Chem Rev, 2020, 120(14): 6878-6933.

[15] [15] BALAISH M, GONZALEZ-ROSILLO J C, KIM K J, et al. Processing thin but robust electrolytes for solid-state batteries[J]. Nat Energy, 2021, 6(3): 227-239.

[16] [16] FAMPRIKIS T, CANEPA P, DAWSON J A, et al. Fundamentals of inorganic solid-state electrolytes for batteries[J]. Nat Mater, 2019, 18(12): 1278-1291.

[17] [17] MANTHIRAM A, YU X, WANG S. Lithium battery chemistries enabled by solid-state electrolytes[J]. Nat Rev Mater, 2017, 2(4): 16103.

[18] [18] ZHAO Q, STALIN S, ZHAO C Z, et al. Designing solid-state electrolytes for safe, energy-dense batteries[J]. Nat Rev Mater, 2020, 5(3): 229-252.

[19] [19] ZOU Z, LI Y, LU Z, et al. Mobile ions in composite solids[J]. Chem Rev, 2020, 120(9): 4169-4221.

[20] [20] LIANG J, LI X, ADAIR K R, et al. Metal halide superionic conductors for all-solid-state batteries[J]. Acc Chem Res, 2021, 54(4): 1023-1033.

[21] [21] HAN G, VASYLENKO A, NEALE A R, et al. Extended condensed ultraphosphate frameworks with monovalent ions combine lithium mobility with high computed electrochemical stability[J]. J Am Chem Soc, 2021, 143(43): 18216-18232.

[22] [22] ZHANG Z, LI H, KAUP K, et al. Targeting superionic conductivity by turning on anion rotation at room temperature in fast ion conductors[J]. Matter, 2020, 2(6): 1667-1684.

[23] [23] JUN K, SUN Y, XIAO Y, et al. Lithium superionic conductors with corner-sharing frameworks[J]. Nat Mater, 2022, 21(8): 924-931.

[24] [24] CHEN L Q, ZHAO Y. From classical thermodynamics to phase-field method[J]. Prog Mater Sci, 2022, 124: 100868.

[25] [25] LIU X, ZHANG X Q, CHEN X, et al. A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries[J]. J Energy Chem, 2022, 68: 548-555.

[26] [26] LUO K, CHEN X, ZHENG H, et al. A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries[J]. J Energy Chem, 2022, 74: 159-173.

[27] [27] KANG Y, LI L, LI B. Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry[J]. J Energy Chem, 2021, 54: 72-88.

[28] [28] CAI J, WANG Z, WU S, et al. A machine learning shortcut for screening the spinel structures of Mg/Zn ion battery cathodes with a high conductivity and rapid ion kinetics[J]. Energy Storage Mater, 2021, 42: 277-285.

[29] [29] LIU Y, GUO B, ZOU X, et al. Machine learning assisted materials design and discovery for rechargeable batteries[J]. Energy Storage Mater, 2020, 31: 434-450.

[30] [30] ZHANG H, WANG Z, REN J, et al. Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning[J]. Energy Storage Mater, 2021, 35: 88-98.

[31] [31] CHEN X, LIU X, SHEN X, et al. Applying machine learning to rechargeable batteries from microscale to macroscale[J]. Angew Chem Int Ed, 2021, 60(46): 24354-24366.

[32] [32] LOMBARDO T, DUQUESNOY M, EL-BOUYSIDY H, et al. Artificial intelligence applied to battery research: Hype or reality?[J]. Chem Rev, 2021, 122(12): 10899-10969.

[33] [33] AYKOL M, HERRING P, ANAPOLSKY A. Machine learning for continuous innovation in battery technologies[J]. Nat Rev Mater, 2020, 5(10): 725-727.

[34] [34] KRAUSKOPF T, RICHTER F H, ZEIER W G, et al. Physicochemical concepts of the lithium metal anode in solid-state batteries[J]. Chem Rev, 2020, 120(15): 7745-7794.

[36] [36] DERINGER V L, BARTóK A P, BERNSTEIN N, et al. Gaussian process regression for materials and molecules[J]. Chem Rev, 2021, 121(16): 10073-10141.

[37] [37] JABLONKA K M, ONGARI D, MOOSAVI S M, et al. Big-Data science in porous materials: Materials genomics and machine learning[J]. Chem Rev, 2020, 120(16): 8066-8129.

[38] [38] POLLICE R, DOS PASSOS GOMES G, ALDEGHI M, et al. Data-driven strategies for accelerated materials design[J]. Acc Chem Res, 2021, 54(4): 849-860.

[39] [39] KRISHNAMURTHY D, WEILAND H, BARATI FARIMANI A, et al. Machine learning based approaches to accelerate energy materials discovery and optimization[J]. ACS Energy Lett, 2018, 4(1): 187-191.

[40] [40] CORREA-BAENA J-P, HIPPALGAONKAR K, VAN DUREN J, et al. Accelerating materials development via automation, machine learning, and high-performance computing[J]. Joule, 2018, 2(8): 1410-1420.

[41] [41] BUTLER K T, DAVIES D W, CARTWRIGHT H, et al. Machine learning for molecular and materials science[J]. Nature, 2018, 559(7715): 547-555.

[42] [42] BATRA R, SONG L, RAMPRASAD R. Emerging materials intelligence ecosystems propelled by machine learning[J]. Nat Rev Mater, 2020, 6(8): 655-678.

[43] [43] BARTóK A P, DE S, POELKING C, et al. Machine learning unifies the modeling of materials and molecules[J]. Sci Adv, 2017, 3(12): e1701816.

[44] [44] BARRETT T D, MALYSHEV A, LVOVSKY A I. Autoregressive neural-network wavefunctions for ab initio quantum chemistry[J]. Nat Mach Intell, 2022, 4(4): 351-358.

[45] [45] DICK S, FERNANDEZ-SERRA M. Machine learning accurate exchange and correlation functionals of the electronic density[J]. Nat Commun, 2020, 11(1): 3509.

[46] [46] XU N, SHI Y, HE Y, et al. A deep-learning potential for crystalline and amorphous Li-Si alloys[J]. J Phys Chem C, 2020, 124(30): 16278-16288.

[47] [47] LU D, WANG H, CHEN M, et al. 86 PFLOPS Deep potential molecular dynamics simulation of 100 million atoms with ab initio accuracy[J]. Comput Phys Commun, 2021, 259: 107624.

[48] [48] WANG H, ZHANG L, HAN J, et al. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics[J]. Comput Phys Commun, 2018, 228: 178-184.

[49] [49] FU Z H, CHEN X, YAO N, et al. The chemical origin of temperature-dependent lithium-ion concerted diffusion in sulfide solid electrolyte Li10GeP2S12[J]. J Energy Chem, 2022, 70: 59-66.

[50] [50] HUANG J, ZHANG L, WANG H, et al. Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors[J]. J Chem Phys, 2021, 154(9): 094703.

[51] [51] LI W, ANDO Y, MINAMITANI E, et al. Study of Li atom diffusion in amorphous Li3PO4 with neural network potential[J]. J Chem Phys, 2017, 147(21): 214106.

[52] [52] HAJIBABAEI A, KIM K S. Universal machine learning interatomic potentials: Surveying solid electrolytes[J]. J Phys Chem Lett, 2021, 12(33): 8115-8120.

[53] [53] FRIEDERICH P, H?SE F, PROPPE J, et al. Machine-learned potentials for next-generation matter simulations[J]. Nat Mater, 2021, 20(6): 750-761.

[54] [54] RAFF L M, MALSHE M, HAGAN M, et al. Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks[J]. J Chem Phys, 2005, 122(8): 084104.

[55] [55] BEHLER J, PARRINELLO M. Generalized neural-network representation of high-dimensional potential-energy surfaces[J]. Phys Rev Lett, 2007, 98(14): 146401.

[56] [56] SCHUCH N, PéREZ-GARCíA D, CIRAC I. Classifying quantum phases using matrix product states and projected entangled pair states[J]. Phys Rev B, 2011, 84(16): 165139.

[57] [57] ZUBATYUK R, SMITH J S, LESZCZYNSKI J, et al. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network[J]. Sci Adv, 2019, 5(8): eaav6490.

[58] [58] ZUO Y, CHEN C, LI X, et al. Performance and cost assessment of machine learning interatomic potentials[J]. J Phys Chem A, 2020, 124(4): 731-745.

[59] [59] BEHLER J, Four Generations of high-dimensional neural network potentials[J]. Chem Rev, 2021, 121(16): 10037-10072.

[60] [60] UNKE O T, CHMIELA S, SAUCEDA H E, et al. Machine learning force fields[J]. Chem Rev, 2021, 121(16): 10142-10186.

[61] [61] ZHANG L, HAN J, WANG H, et al. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics[J]. Phys Rev Lett, 2018, 120(14): 143001.

[62] [62] MIWA K, ASAHI R. Molecular dynamics simulations with machine learning potential for Nb-doped lithium garnet-type oxide Li7?xLa3(Zr2?xNbx)O12[J]. Phys Rev Mater, 2018, 2(10): 105404.

[63] [63] MIWA K, ASAHI R. Molecular dynamics simulations of lithium superionic conductor Li10GeP2S12 using a machine learning potential[J]. Solid State Ionics, 2021, 361: 115567.

[64] [64] RAO K K, YAO Y, GRABOW L C. Accelerated modeling of lithium diffusion in solid state electrolytes using artificial neural networks[J]. Adv Theory Simulat, 2020, 3(9): 2000097.

[65] [65] LI F, CHENG X, LU L L, et al. Stable all-solid-state lithium metal batteries enabled by machine learning simulation designed halide electrolytes[J]. Nano Lett, 2022, 22(6): 2461-2469.

[66] [66] WANG C, AOYAGI K, WISESA P, et al. Lithium Ion conduction in cathode coating materials from on-the-fly machine learning[J]. Chem Mater, 2020, 32(9): 3741-3752.

[67] [67] SENDEK A D, YANG Q, CUBUK E D, et al. Holistic computational structure screening of more than 12?000 candidates for solid lithium-ion conductor materials[J]. Energy Environ Sci, 2017, 10(1): 306-320.

[68] [68] ZHANG Y, HE X, CHEN Z, et al. Unsupervised discovery of solid-state lithium ion conductors[J]. Nat Commun, 2019, 10(1): 5260.

[69] [69] FUJIMURA K, SEKO A, KOYAMA Y, et al. Accelerated materials design of lithium superionic conductors based on first-principles calculations and machine learning algorithms[J]. Adv Energy Mater, 2013, 3(8): 980-985.

[70] [70] XU Y, ZONG Y, HIPPALGAONKAR K. Machine learning-assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors using facile descriptors[J]. J Phys Commun, 2020, 4(5): 055015.

[71] [71] VERDUZCO J C, MARINERO E E, STRACHAN A. An active learning approach for the design of doped LLZO ceramic garnets for battery applications[J]. Integr Mater Manuf Innov, 2021, 10(2): 299-310.

[72] [72] ZHAO Q, AVDEEV M, CHEN L, et al. Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors[J]. Sci Bull, 2021, 66(14): 1401-1408.

[73] [73] SHEN X, ZHANG R, SHI P, et al. How does external pressure shape Li dendrites in li metal batteries?[J]. Adv Energy Mater, 2021, 11(10): 2003416.

[74] [74] SHEN X, ZHANG R, CHEN X, et al. The failure of solid electrolyte interphase on Li metal anode: Structural uniformity or mechanical strength?[J]. Adv Energy Mater, 2020, 10(10): 1903645.

[75] [75] SU X, GUO K, MA T, et al. Deformation and chemomechanical degradation at solid electrolyte-electrode interfaces[J]. ACS Energy Lett, 2017, 2(8): 1729-1733.

[76] [76] SUN N, LIU Q, CAO Y, et al. Anisotropically electrochemo- mechanical evolution in solid-state batteries and interfacial tailored strategy[J]. Angew Chem Int Ed, 2019, 58(51): 18647-18653.

[77] [77] ZHANG X, LIU T, ZHANG S, et al. Synergistic coupling between Li6.75La3Zr1.75Ta0.25O12 and poly(vinylidene fluoride) induces high ionic conductivity, mechanical strength, and thermal stability of solid composite electrolytes[J]. J Am Chem Soc, 2017, 139(39): 13779-13785.

[78] [78] DIXIT M B, SINGH N, HORWATH J P, et al. In situ investigation of chemomechanical effects in thiophosphate solid electrolytes[J]. Matter, 2020, 3(6): 2138-2159.

[79] [79] FU C, VENTURI V, KIM J, et al. Universal chemomechanical design rules for solid-ion conductors to prevent dendrite formation in lithium metal batteries[J]. Nat Mater, 2020, 19(7): 758-766.

[80] [80] MONROE C, NEWMAN J. The impact of elastic deformation on deposition kinetics at lithium/polymer interfaces[J]. J Electrochem Soc, 2005, 152(2): A396.

[81] [81] JO J, CHOI E, KIM M, et al. Machine learning-aided materials design platform for predicting the mechanical properties of Na-ion solid-state electrolytes[J]. ACS Appl Energy Mater, 2021, 4(8): 7862-7869.

[82] [82] CHOI E, JO J, KIM W, et al. Searching for mechanically superior solid-state electrolytes in Li-ion batteries via data-driven approaches[J]. ACS Appl Mater Interfaces, 2021, 13(36): 42590-42597.

[83] [83] 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.

[84] [84] CHEN Y T, DUQUESNOY M, TAN D H S, et al. Fabrication of high-quality thin solid-state electrolyte films assisted by machine learning[J]. ACS Energy Lett, 2021, 6(4): 1639-1648.

[85] [85] MAHBUB R, HUANG K, JENSEN Z, et al. Text mining for processing conditions of solid-state battery electrolytes[J]. Electrochem Commun, 2020, 121: 106860.

[86] [86] DIXIT M B, VERMA A, ZAMAN W, et al. Synchrotron imaging of pore formation in Li metal solid-state batteries aided by machine learning[J]. ACS Appl Energy Mater, 2020, 3(10): 9534-9542.

[87] [87] ZHAO Y, SCHIFFMANN N, KOEPPE A, et al. Machine learning assisted design of experiments for solid state electrolyte lithium aluminum titanium phosphate[J]. Front Mater, 2022: DOI: 10.3389/fmats.2022.821817.

[88] [88] HATAKEYAMA-SATO K, TEZUKA T, NISHIKITANI Y, et al. Synthesis of lithium-ion conducting polymers designed by machine learning-based prediction and screening[J]. Chem Lett, 2019, 48(2): 130-132.