Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode
Source abstract
Abstract
Abstract Although the electrode-electrolyte interface is a crucial electrochemical region, the comprehensive understanding of interface reactions is limited by the time and space scales of experimental tools. Theoretical simulations with this delicate interface also remain one of the most significant challenges for atomistic modeling, particularly for the stable long-timescale simulation of the interface. Here we introduce a novel scheme, hybrid ab initio molecular dynamics combined with machine learning potential (HAML), to accelerate the modeling of electrode-electrolyte interface reactions. We demonstrate its effectiveness in modeling the interfaces of Li metal with both liquid and solid-state electrolytes, capturing critical processes over extended time scales. Furthermore, we reveal the role of interface reaction kinetics in interface regulation through HAML simulations, combined with the similarity analysis method. It is demonstrated that element (Se, F, O) doping in the Li6PS5Cl system is an effective strategy for enhancing interface reaction kinetics, facilitating the formation of a more stable interface protective layer faster at room temperature. Moreover, moderate structural instability can positively contribute to interface stabilization. HAML offers a promising approach for addressing the challenge of designing stable interfaces while reducing computational costs. This work provides valuable insights for advancing the understanding and optimization of interface behaviors in Li metal batteries.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Battery Materials and Technologies · Advancements in Battery Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (73)
Show source-provided references
1. Seh, Z. W. et al. Combining theory and experiment in electrocatalysis: Insights into materials design. Science 355, eaad4998 (2017).10.1126/science.aad4998 ↗
2. Salanne, M. et al. Efficient storage mechanisms for building better supercapacitors. Nat. Energy 1, 1–10 (2016).10.1038/nenergy.2016.70 ↗
3. Scalfi, L., Salanne, M. & Rotenberg, B. Molecular simulation of electrode-solution interfaces. Annu. Rev. Phys. Chem. 72, 189–212 (2021).10.1146/annurev-physchem-090519-024042 ↗
4. Tarascon, J.-M. & Armand, M. Issues and challenges facing rechargeable lithium batteries. Nature 414, 359–367 (2001).10.1038/35104644 ↗
5. Albertus, P., Babinec, S., Litzelman, S. & Newman, A. Status and challenges in enabling the lithium metal electrode for high-energy and low-cost rechargeable batteries. Nat. Energy 3, 16–21 (2018).10.1038/s41560-017-0047-2 ↗
6. Liu, J. et al. Pathways for practical high-energy long-cycling lithium metal batteries. Nat. Energy 4, 180–186 (2019).10.1038/s41560-019-0338-x ↗
7. Liu, B., Zhang, J.-G. & Xu, W. Advancing lithium metal batteries. Joule 2, 833–845 (2018).10.1016/j.joule.2018.03.008 ↗
8. Qian, J. et al. High rate and stable cycling of lithium metal anode. Nat. Commun. 6, 6362 (2015).10.1038/ncomms7362 ↗
9. Epp, V., Gün, Ö, Deiseroth, H.-J. & Wilkening, M. Highly mobile ions: low-temperature NMR directly probes extremely fast Li+ hopping in argyrodite-type Li6PS5. Br. J. Phys. Chem. Lett. 4, 2118–2123 (2013).10.1021/jz401003a ↗
10. Chen, H. M., Maohua, C. & Adams, S. Stability and ionic mobility in argyrodite-related lithium-ion solid electrolytes. Phys. Chem. Chem. Phys. 17, 16494–16506 (2015).10.1039/C5CP01841B ↗
11. Xu, Y. et al. Promoting mechanistic understanding of lithium deposition and solid-electrolyte interphase (SEI) formation using advanced characterization and simulation methods: recent progress, limitations, and future perspectives. Adv. Energy Mater. 12, 2200398 (2022).10.1002/aenm.202200398 ↗
12. Wagner-Henke, J. et al. Knowledge-driven design of solid-electrolyte interphases on lithium metal via multiscale modelling. Nat. Commun. 14, 6823 (2023).10.1038/s41467-023-42212-7 ↗
13. Yao, N., Chen, X., Fu, Z.-H. & Zhang, Q. Applying classical, ab initio, and machine-learning molecular dynamics simulations to the liquid electrolyte for rechargeable batteries. Chem. Rev. 122, 10970–11021 (2022).10.1021/acs.chemrev.1c00904 ↗
14. Qi, J., Ko, T. W., Wood, B. C., Pham, T. A. & Ong, S. P. Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling. npj Comput. Mater. 10, 43 (2024).10.1038/s41524-024-01227-4 ↗
15. Young, J., Kulick, P. M., Juran, T. R. & Smeu, M. Comparative study of ethylene carbonate-based electrolyte decomposition at Li, Ca, and Al anode interfaces. ACS Appl. Energy Mater. 2, 1676–1684 (2019).10.1021/acsaem.8b01707 ↗
16. Leung, K. Electronic structure modeling of electrochemical reactions at electrode/electrolyte interfaces in lithium ion batteries. J. Phys. Chem. C117, 1539–1547 (2013).10.1021/jp308929a ↗
17. Camacho-Forero, L. E., Smith, T. W. & Balbuena, P. B. Effects of high and low salt concentration in electrolytes at lithium–metal anode surfaces. J. Phys. Chem. C121, 182–194 (2017).10.1021/acs.jpcc.6b10774 ↗
18. Camacho-Forero, L. E. & Balbuena, P. B. Effects of charged interfaces on electrolyte decomposition at the lithium metal anode. J. Power Sources 472, 228449 (2020).10.1016/j.jpowsour.2020.228449 ↗
19. Behler, J. Perspective. Machine learning potentials for atomistic simulations. J. Chem. Phys. 145, 170901 (2016).10.1063/1.4966192 ↗
20. Mishin, Y. Machine-learning interatomic potentials for materials science. Acta Mater. 214, 116980 (2021).10.1016/j.actamat.2021.116980 ↗
21. Zuo, Y. et al. Performance and cost assessment of machine learning interatomic potentials. J. Phys. Chem. A 124, 731–745 (2020).10.1021/acs.jpca.9b08723 ↗
22. Liu, Y. et al. Data quantity governance for machine learning in materials science. Natl. Sci. Rev. 10, nwad125 (2023).10.1093/nsr/nwad125 ↗
23. Deringer, V. L., Caro, M. A. & Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mater. 31, 1902765 (2019).10.1002/adma.201902765 ↗
24. Qi, J. et al. Machine learning moment tensor potential for modeling dislocation and fracture in L1 0-TiAl and D0 19-Ti 3 Al alloys. Phys. Rev. Mater. 7, 103602 (2023).10.1103/PhysRevMaterials.7.103602 ↗
25. Lai, G. et al. The mechanism of external pressure suppressing dendrites growth in Li metal batteries. J. Energy Chem. 79, 489–494 (2023).10.1016/j.jechem.2023.01.003 ↗
26. Lai, G. et al. The mechanism of Li deposition on the Cu substrates in the anode-free Li metal batteries. Small 19, 2205416 (2023).10.1002/smll.202205416 ↗
27. Lee, T. et al. Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li0. 375Sr0. 4375Ta0. 75Zr0. 25O3. Nat. Commun. 14, 1940 (2023).10.1038/s41467-023-37115-6 ↗
28. Qi, J. et al. Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors. Mater. Today Phys. 21, 100463 (2021).10.1016/j.mtphys.2021.100463 ↗
29. Gong, S. et al. A predictive machine learning force-field framework for liquid electrolyte development. Nature Machine Intelligence 7, 534–552 (2025).10.1038/s42256-025-01009-7 ↗
30. Holekevi Chandrappa, M. L., Qi, J., Chen, C., Banerjee, S. & Ong, S. P. Thermodynamics and kinetics of the cathode–electrolyte interface in all-solid-state Li–S batteries. J. Am. Chem. Soc. 144, 18009–18022 (2022).10.1021/jacs.2c07482 ↗
31. Ko, T. W. & Ong, S. P. Recent advances and outstanding challenges for machine learning interatomic potentials. Nat. Comput. Sci. 3, 998–1000 (2023).10.1038/s43588-023-00561-9 ↗
32. Liu, Y. & Mo, Y. Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials. npj Comput. Mater. 10, 159 (2024).10.1038/s41524-024-01333-3 ↗
33. Sundararaman, R. & Schwarz, K. Evaluating continuum solvation models for the electrode-electrolyte interface: Challenges and strategies for improvement. J. Chem. Phys. 146, 084111 (2017).10.1063/1.4976971 ↗
34. Vandermause, J. et al. On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events. npj Comput. Mater. 6, 20 (2020).10.1038/s41524-020-0283-z ↗
35. Kulichenko, M. et al. Uncertainty-driven dynamics for active learning of interatomic potentials. Nat. Comput. Sci. 3, 230–239 (2023).10.1038/s43588-023-00406-5 ↗
36. van der Oord, C., Sachs, M., Kovács, D. P., Ortner, C. & Csányi, G. Hyperactive learning for data-driven interatomic potentials. npj Comput. Mater. 9, 168 (2023).10.1038/s41524-023-01104-6 ↗
37. Yang, Y., Zhang, S., Ranasinghe, K. D., Isayev, O. & Roitberg, A. E. Machine learning of reactive potentials. Annu. Rev. Phys. Chem. 75, 371–395 (2024).10.1146/annurev-physchem-062123-024417 ↗
38. Tharwat, A. & Schenck, W. A survey on active learning: State-of-the-art, practical challenges and research directions. Mathematics 11, 820 (2023).10.3390/math11040820 ↗
39. Friederich, P., Häse, F., Proppe, J. & Aspuru-Guzik, A. Machine-learned potentials for next-generation matter simulations. Nat. Mater. 20, 750–761 (2021).10.1038/s41563-020-0777-6 ↗
40. Fu, X. et al. Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations. Preprint at https://arxiv.org/abs/2210.07237 (2022).
41. Cui, T. et al. Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data. Nat. Commun. 16, 1891 (2025).10.1038/s41467-025-57101-4 ↗
42. Xu, L., Shao, W., Jin, H. & Wang, Q. Data Efficient and stability indicated sampling for developing reactive machine learning potential to achieve ultralong simulation in lithium-metal batteries. J. Phys. Chem. C127, 24106–24117 (2023).10.1021/acs.jpcc.3c05522 ↗
43. Liu, Y. et al. Effects of high and low salt concentrations in electrolytes at lithium–metal anode surfaces using DFT-ReaxFF hybrid molecular dynamics method. J. Phys. Chem. Lett. 12, 2922–2929 (2021).10.1021/acs.jpclett.1c00279 ↗
44. Shapeev, A. V. Moment tensor potentials: a class of systematically improvable interatomic potentials. Multiscale Model. Simul. 14, 1153–1173 (2016).10.1137/15M1054183 ↗
45. Novikov, I. S., Gubaev, K., Podryabinkin, E. V. & Shapeev, A. V. The MLIP package: moment tensor potentials with MPI and active learning. Mach. Learn. Sci. Technol. 2, 025002 (2020).10.1088/2632-2153/abc9fe ↗
46. Wan, C. et al. Multinuclear NMR study of the solid electrolyte interface formed in lithium metal batteries. ACS Appl. Mater. Interfaces 9, 14741–14748 (2017).10.1021/acsami.6b15383 ↗
47. Chen, Y. et al. Origin of dendrite-free lithium deposition in concentrated electrolytes. Nat. Commun. 14, 2655 (2023).10.1038/s41467-023-38387-8 ↗
48. Zhang, H., Shen, C., Huang, Y. & Liu, Z. Spontaneously formation of SEI layers on lithium metal from LiFSI/DME and LiTFSI/DME electrolytes. Appl. Surf. Sci. 537, 147983 (2021).10.1016/j.apsusc.2020.147983 ↗
49. Deiseroth, H. J. et al. Li6PS5X: a class of crystalline Li-rich solids with an unusually high Li+ mobility. Angew. Chem. Int. Ed. 47, 755–758 (2008).10.1002/anie.200703900 ↗
50. Rao, R. P. & Adams, S. Studies of lithium argyrodite solid electrolytes for all-solid-state batteries. Phys. Status Solidi A 208, 1804–1807 (2011).10.1002/pssa.201001117 ↗
51. Deng, Z., Zhu, Z., Chu, I.-H. & Ong, S. P. Data-driven first-principles methods for the study and design of alkali superionic conductors. Chem. Mater. 29, 281–288 (2016).10.1021/acs.chemmater.6b02648 ↗
52. Holekevi Chandrappa, M. L., Qi, J., Chen, C., Banerjee, S. & Ong, S. P. Thermodynamics and kinetics of the cathode-electrolyte interface in all-solid-state Li-S batteries. J. Am. Chem. Soc. 144, 18009–18022 (2022).10.1021/jacs.2c07482 ↗
53. Jalem, R., Chandrappa, M. L. H., Qi, J., Tateyama, Y. & Ong, S. P. Lithium dynamics at grain boundaries of β-Li3PS4 solid electrolyte. Energy Adv. 2, 2029–2041 (2023).10.1039/D3YA00234A ↗
54. Richards, W. D., Miara, L. J., Wang, Y., Kim, J. C. & Ceder, G. Interface stability in solid-state batteries. Chem. Mater. 28, 266–273 (2015).10.1021/acs.chemmater.5b04082 ↗
55. Cheng, T., Merinov, B. V., Morozov, S. & Goddard, W. A. Quantum mechanics reactive dynamics study of solid Li-electrode/Li6PS5Cl-electrolyte interface. ACS Energy Lett. 2, 1454–1459 (2017).10.1021/acsenergylett.7b00319 ↗
56. Yu, C. et al. Unravelling Li-ion transport from picoseconds to seconds: bulk versus interfaces in an argyrodite Li6PS5Cl-Li2S all-solid-state Li-Ion battery. J. Am. Chem. Soc. 138, 11192–11201 (2016).10.1021/jacs.6b05066 ↗
57. Wenzel, S., Sedlmaier, S. J., Dietrich, C., Zeier, W. G. & Janek, J. Interfacial reactivity and interphase growth of argyrodite solid electrolytes at lithium metal electrodes. Solid State Ion. 318, 102–112 (2018).10.1016/j.ssi.2017.07.005 ↗
58. Golov, A. & Carrasco, J. Molecular-level insight into the interfacial reactivity and ionic conductivity of a Li-argyrodite Li6PS5Cl solid electrolyte at bare and coated Li-metal anodes. ACS Appl. Mater. Interfaces 13, 43734–43745 (2021).10.1021/acsami.1c12753 ↗
59. Chaney, G., Golov, A., van Roekeghem, A., Carrasco, J. & Mingo, N. Two-step growth mechanism of the solid electrolyte interphase in argyrodyte/Li-metal contacts. ACS Appl. Mater. Interfaces 16, 24624–24630 (2024).10.1021/acsami.4c02548 ↗
60. Arnold, W. et al. Synthesis of fluorine-doped lithium argyrodite solid electrolytes for solid-state lithium metal batteries. ACS Appl. Mater. Interfaces 14, 11483–11492 (2022).10.1021/acsami.1c24468 ↗
61. Wu, M., Liu, G. & Yao, X. Oxygen doped argyrodite electrolyte for all-solid-state lithium batteries. Appl. Phys. Lett. 121, 203904 (2022).10.1063/5.0114275 ↗
62. Golov, A. & Carrasco, J. Unveiling solid electrolyte interphase formation at the molecular level: computational insights into bare Li-metal anode and Li6PS5-xSexCl argyrodite solid electrolyte. ACS Energy Lett. 8, 4129–4135 (2023).10.1021/acsenergylett.3c01363 ↗
63. Kim, H.-M., Subramanian, Y. & Ryu, K.-S. Improved electrochemical and air stability performance of SeS2 doped argyrodite lithium superionic conductors for all-solid-state lithium batteries. Electrochim. Acta 442, 141869 (2023).10.1016/j.electacta.2023.141869 ↗
64. Camacho-Forero, L. E. & Balbuena, P. B. Elucidating interfacial phenomena between solid-state electrolytes and the sulfur-cathode of lithium–sulfur batteries. Chem. Mater. 32, 360–373 (2019).10.1021/acs.chemmater.9b03880 ↗
65. Song, Z. et al. Promoting high-voltage stability through local lattice distortion of halide solid electrolytes. Nat. Commun. 15, 1481 (2024).10.1038/s41467-024-45864-1 ↗
66. Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).10.1103/PhysRevB.54.11169 ↗
67. Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6, 15–50 (1996).10.1016/0927-0256(96)00008-0 ↗
68. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).10.1103/PhysRevLett.77.3865 ↗
69. Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994).10.1103/PhysRevB.50.17953 ↗
70. Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 132, 154104 (2010).10.1063/1.3382344 ↗
71. Martyna, G. J., Klein, M. L. & Tuckerman, M. Nosé–Hoover chains: the canonical ensemble via continuous dynamics. J. Chem. Phys. 97, 2635–2643 (1992).10.1063/1.463940 ↗
72. Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995).10.1006/jcph.1995.1039 ↗
73. Momma, K. & Izumi, F. VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data. J. Appl. Crystallogr. 44, 1272–1276 (2011).10.1107/S0021889811038970 ↗