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The [EMIm]+Cl-+AlCl3 ion liquid is a promising prototype electrolyte for aluminumion batteries (AIBs). Its ionic transport behavior involves multiple mobile species (Al3+、 AlCl3 、 [AlCl4]- and [Al2Cl7]-), with experimentally unresolved ion migration mechanisms and conversion reactions among these species. This complexity results in heterogeneous ion migration mechanisms and sluggish diffusion kinetics, which cannot be accurately and reliably captured by the conventional first-principles molecular dynamics (FPMD) simulations within the very limited time duration (tens of ps) and relatively small modelling structure (less than 103 atoms). Otherwise, classic molecular dynamics simulations based on various force fields are also scarce for studying and predicting the atomic structure evolution and ion diffusion dynamics of the complex electrolyte system such as ion liquids. In this work, we successfully developed deep neural network interatomic potential (DP-potential) through machine learning techniques, combining first-principles accuracy with classical molecular dynamics efficiency, to systematically investigate various chemical and physical properties for [EMIm]+Cl-+AlCl3 ion-liquid at finite temperature. Training and validation of DP potential for [EMIm]+Cl-+AlCl3 ion liquid are implemented with a two-stage protocol, including the primary training stage and the refining stage. Before initiating the two training stages, a series of first-principles molecular dynamics (FPMD) simulations are performed for [EMIm]+Cl-+AlCl3 ion liquids with different molar ratios (1.0, 1.3, 1.5, 1.7 and 2.0) and equilibrium densities (1.09 g/cm3 ~ 1.56 g/cm3) at finite temperatures (300 K and 400 K), resulting in a highly diverse training datasets spanning a board range of chemical composition and density during the primary training stage for DP potential. Then, the trained DP-potential is employed to conduct long-timescale classic molecular dynamics simulations using LAMMPS program for the [EMIm]+Cl-+AlCl3 ion liquids to produce the atomic configurations that either show significant errors in the calculated atomic forces and total energies or exhibit the unusual atomic evolution before crashing. Those highly extrapolated atomic configurations are merged with the initial training datasets to reoptimize the DP potential in the second refining stage. Through this two-stage training approach, we successfully constructed a deep learning neural network interatomic potential with high accuracy, achieving energy prediction errors of 5×10-4 eV/atom and force prediction errors of 5×10-2 eV/Å. The reliability of the finally obtained machine learning potential is further validated through a systematic comparison of radial distribution functions (RDF) for some representative atomic pairs such as C-N, C-H, Al-Cl and Cl-H, obtained from both DP-MD and FPMD, demonstrating excellent consistency for the results between two methods. The DP-MD simulations are systematically carried out to investigate vibrational spectrum and Al3+ diffusion dynamics as well as possible conversion reactions among molecular or ionic species (Al3+、AlCl3、[AlCl4]- and [Al2Cl7]-) in [EMIm]+Cl-+AlCl3 ion liquids within 104 atoms at finite temperature. From the calculated vibrational density of states (VDOS), it is revealed that the VDOS of [EMIm]+Cl-+AlCl3 ion liquids could be approximated as the simple superposition of vibrational spectra of individual species ([EMIm]+, [AlCl4]-, and [Al2Cl7]-) with the dominance of H related vibrational modes above 90 THz and the Al-Cl modes below 20 THz. At 300 K, DP-MD predicts that Al3+ diffusion coefficients remain consistently around 4×10-7 cm2/s at 300 K regardless of the chemical compositions, and the estimated diffusion activation energy of ~0.20 eV closely matching experimental measurements (0.15 eV). In addition, the calculated ionic conductivity of 27.37 mS/cm for [EMIm]+Cl-+AlCl3 at room temperature, and which is only 18.2% deviated from the experimental value (23.15 mS/cm). Notably, two distinct Al3+ diffusion mechanisms are identified in [EMIm]+Cl-+AlCl3 ion liquid: (1) direct migration processes conducted solely by molecular species including [AlCl4]- and [Al2Cl7]-; (2) the migration of the neutral AlCl3 molecule mediated with two neighboring [AlCl4]- anions through the conversion reaction between [Al2Cl7]- and AlCl3+[AlCl4]- moieties. Furthermore, first-principles calculations on the probable dissociation pathways of [Al2Cl7]- revealed from DP-MD predict a reaction energy barrier height of 0.49 eV for the AlCl3 transferring between two [AlCl4]- anions with an increased reaction probability from 0.00047 events/(ps·Al3+) at 1:1.3 molar ratio to 0.00347 events/(ps·Al3+) at 1:1.75 molar. Overall, we have successfully proposed a highly efficient and reliable workflow to train and validate the deep neural network interatomic potential for complex electrolyte system such as [EMIm]+Cl-+AlCl3 ion liquids, thus providing a more comprehensive investigation of Al3+ transport mechanisms in ionic liquid electrolytes for aluminum-ion batteries. In conclusion, this work could further advance the application of machine learning-based potentials in simulating electrolyte systems characterized by complex molecular architectures and sluggish diffusion dynamics.
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[1] Das A, Balakrishnan N T M, Sreeram P, Fatima M J J, Joyner J D, Thakur V K, Pullanchiyodan A, Ahn J, Raghavan P 2024 Coordin. Chem. Rev. 502 215593
[2] Taghavi-Kahagh A, Roghani-Mamaqani H, Salami-Kalajahi M 2024 J. Energy Chem. 90 77
[3] Muoz-Torrero D, Jesús Palma, Marcilla R, Ventosa 2019 Dalton T. 48 9906.
[4] Liu H L, Wang H Y, Jing Z A, Wu K, Cheng Y H, Xiao B 2020 J. Phys. Chem. C 124 25769
[5] Stephanie R, Park C Y, Shinde P A, Alhajri E, Chodankar N R, Park T J 2024 Energy Storage Mater. 68 103336
[6] Manna S S, Bhauriyal P, Pathak B 2020 Mater. Adv. 1 1354
[7] Yan C S, Lv C D, Wang L G, Cui W, Zhang L Y, Dinh K N, Tan H T, Wu C, Wu T P, Ren Y, Chen J Q, Liu Z, Srinivasan M, Rui X H, Yan Q Y, Yu G H 2020 J. Am. Chem. Soc. 142 15295
[8] Lin M C, Gong M, Lu B G, Wu Y P, Wang D Y, Guan M Y, Angell M, Chen C X, Yang J, Hwang B J, Dai H J 2015 Nature 520 324
[9] Elterman V A, Borozdin A V, Druzhinin K D, Il’ina E A, Shevelin P Y, Yolshina L A 2024 J. Mol. Liq. 394 123702
[10] Lv Z C, Han M, Sun J H, Hou L X, Chen H, Li Y X, Lin M C 2019 J. Power Sources 418 233
[11] Ng K L, Malik M, Buch E, Glossmann T, Hintennach A, Azimi G 2019 Electrochimica Acta 327 135031
[12] Lv T S, Suo L M 2021 Curr. Opin. Electrochem. 29 100818
[13] Gao T, Li X G, Wang X W, Hu J K, Han F D, Fan X L, Suo L M, Pearse A J, Lee S B, Rubloff G W, Gaskell K J, Noked M, Wang C S 2016 Angew. Chem. 128 10052
[14] Lei H P, Wang M Y, Tu J G, Jiao S Q 2019 Sustain. Energ. Fuels 3 2717
[15] Huo X G, Zhang B, Li J L, Wang X X, Qin T, Zhang Y, Kang F Y 2021 ACS Appl. Mater. Interfaces 13 11822
[16] Ahmed A N, Nahian M K, Reddy R G 2024 Metall. Mater. Trans. B 55 1754
[17] Liu H L, Zeng C, Jing Z A, Wu K, Cheng Y H, Xiao B 2025 Nanoscale 17 5375
[18] Li J, Zeng F, El-Demellawi J K, Lin Q, Xi S, Wu J, Tang J, Zhang X, Liu X, Tu S 2022 ACS Appl. Mater. Interfaces 14 45254
[19] Kosar M, Taimoory S M, Diesenhaus O, Trant J F 2023 J. Chem. Phys. 159 144503.
[20] Elterman V A, Shevelin P Y, Yolshina L A, Borozdin A V 2022 J. Mol. Liq. 364 120061
[21] Senftle T P, Hong S, Islam M M 2016 npj Comput. Mater. 2 15011
[22] Jain A 2024 Curr. Opin. Solid St. M. 33 101189
[23] Poltavsky I, Tkatchenko A. 2021 J. Phys. Chem. Lett. 12 6551
[24] Song R, Liu X M, Wang H B, Lv H, Song X Y 2024 Acta Phys. Sin. 73 126201 [宋 睿, 刘雪梅, 王海滨, 吕皓, 宋晓艳. 2024 物理学报 73 126201]
[25] Botu V, Batra R, Chapman J, Ramprasad R 2017 J. Phys. Chem. C 121 511
[26] Wang P J, Fan J Y, Su Y, Zhao J J 2020 Acta Phys.Sin. 69 238702 [王鹏举, 范俊 宇, 苏艳, 赵纪军 2020 物理学报 69 238702]
[27] Ouyang X J, Zhang Y X, Wang Z L, Zhang F, Chen W J, Zhuang Y, Jie X, Liu L J, Wang D W 2024 Acta Phys. Sin. 73 086301 [欧阳鑫健, 张岩星, 王之龙, 张锋, 陈韦嘉, 庄园, 揭晓, 刘来君, 王大威., 2024 物理学报 73 086301]
[28] Li Z Q, Tan X Y, Duan X L, Zhang J Y, Yang J Y 2022 Acta Phys. Sin. 71 247803[李志强, 谭晓瑜, 段忻磊, 张敬义, 杨家跃. 2022 物理学报 71 247803]
[29] Zhu D, Li Sheng, Hu T P, Chen S, Shi M C, Hua H M, Yang K, Wang J L, Tang Y P, He X M, Xu H et al. 2024 J. Phys. Chem. Lett. 15 4024
[30] Zheng Z Y, Zhou J, Zhu Y S 2024 Chem. Soc. Rev. 53 3134
[31] Wu M W, Wei Z, Zhao Y, He Q 2025 Nanomaterials 15 22
[32] Gong S, Zhang Y M, Mu Z L, Pu Z C, Wang H Y, Han X, Yu Z A, Chen M Y, Zhang T Z, Wang Z, Chen L F, Yang Z Z, Wu X J, Shi S C, Gao W H, Yan W, Xiang L 2025 Nat. mach. Intell. 7 543
[33] Thomas D. Kühne T D, Iannuzzi M, Mauro Del Ben M D, Rybkin V V, Seewalk P, Stein F, Laino T, Khaliullin R Z, Scheütt O, Schiffmann F, Golze D, Wilhelm J, Chulkov S, Bani-Hashemian M H, Weber V, Borštnik U, Taillefumier M, Jakobovits AS, Lazzaro A, Pabst H, Müller T, Schade R, Guidon M, Andermatt S, Holmberg N, Schenter G K, Hehn A, Bussy A, Belleflamme F, Tabacchi G, Glöß A, Lass M, Bethune I, Mundy C J, Plessl C, Watkins M, VandeVondele J, Krack M, Hutter J 2020 J. Chem. Phys. 152 194103
[34] Zeng J Z, Zhang D, Peng A Y, Zhang X Y, He S S, Wang Y, Liu X Z J, Bi H R, Li Y F, Cai C, Zhang C Q, Du Y M, Zhu J X, Mo P H, Huang Z T, Zeng Q Y, Shi S C, Qin X J, Yu Z X, Luo C X, Ding Y, Liu Y P, Shi R S, Wang Z Y, Bore S L, Chang J H, Deng Z, Ding Z H, Han S Y, Jiang W R, Ke G J, Liu Z Q, Lu D H, Muraoka K, Oliaei H, Singh A K, Que H H, Xu W H, Xu Z M C, Zhuang Y B, Dai J Y, Giese T J, Jia W L, Xu B, York D M, Zhang L F, Wang H 2025 J. Chem. Theory Comput. 21 4375
[35] Martínez L, Andrade R, Birgin E G, Martínez J M 2009 J. Comput. Chem. 30 2157
[36] Perdew J P, Burke K, Ernzerhof M 1996 Phys. Rev. Lett. 78 1396
[37] VandeVondele J, Hutter J 2007 J. Chem. Phys. 127 114105
[38] Goedecker S, Teter M, and Hutter J. 1996 Phys. Rev. B 54 1703
[39] Grimme S, Antony J, Ehrlich S, Krieg H 2010 J. Chem. Phys 132 154104
[40] Grimme S, Ehrlich S, Goerigk L. 2011 J. Comput. Chem. 32 1456
[41] Thompson A P, Aktulga H M, Berger R, Bolintineanu D S, Brown W M, Crozier P S, Veld P J, Kohlmeyer A, Moore S G, Nguyen T D, Shan R, Stevens M J, Tranchida J, Trott C, Plimpton S J 2022 Comput. Phys. Commun. 271 10817
[42] Dickey J M and Paskin A. 1969 Phys. Rev. 188 1407
[43] Manz T A. 2017 RSC Adv. 7 45552-45581.
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