Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode
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 significan...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01747-7 |
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| author | Genming Lai Ruiqi Zhang Chi Fang Juntao Zhao Taowen Chen Yunxing Zuo Bo Xu Jiaxin Zheng |
| author_facet | Genming Lai Ruiqi Zhang Chi Fang Juntao Zhao Taowen Chen Yunxing Zuo Bo Xu Jiaxin Zheng |
| author_sort | Genming Lai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-725b54bcfb8f4af8a470a090dc7d1223 |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-725b54bcfb8f4af8a470a090dc7d12232025-08-20T03:05:15ZengNature Portfolionpj Computational Materials2057-39602025-07-011111810.1038/s41524-025-01747-7Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anodeGenming Lai0Ruiqi Zhang1Chi Fang2Juntao Zhao3Taowen Chen4Yunxing Zuo5Bo Xu6Jiaxin Zheng7School of Advanced Materials, Peking University, Shenzhen Graduate SchoolSchool of Advanced Materials, Peking University, Shenzhen Graduate SchoolSchool of Advanced Materials, Peking University, Shenzhen Graduate SchoolSchool of Advanced Materials, Peking University, Shenzhen Graduate SchoolEACOMPEACOMPGAC Aion New Energy Automobile Co., Ltd.School of Advanced Materials, Peking University, Shenzhen Graduate SchoolAbstract 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.https://doi.org/10.1038/s41524-025-01747-7 |
| spellingShingle | Genming Lai Ruiqi Zhang Chi Fang Juntao Zhao Taowen Chen Yunxing Zuo Bo Xu Jiaxin Zheng Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode npj Computational Materials |
| title | Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode |
| title_full | Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode |
| title_fullStr | Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode |
| title_full_unstemmed | Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode |
| title_short | Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode |
| title_sort | machine learning accelerated mechanistic exploration of interface modification in lithium metal anode |
| url | https://doi.org/10.1038/s41524-025-01747-7 |
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