A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy
Abstract Solid electrolytes with fast ion transport are crucial for solid state lithium metal batteries. Chemical doping has been the most effective strategy for improving ion condictiviy, and atomistic simulation with machine-learning potentials helps optimize doping by predicting ion conductivity...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01764-6 |
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| author | Ruoyu Wang Mingyu Guo Yuxiang Gao Xiaoxu Wang Yuzhi Zhang Bin Deng Mengchao Shi Linfeng Zhang Zhicheng Zhong |
| author_facet | Ruoyu Wang Mingyu Guo Yuxiang Gao Xiaoxu Wang Yuzhi Zhang Bin Deng Mengchao Shi Linfeng Zhang Zhicheng Zhong |
| author_sort | Ruoyu Wang |
| collection | DOAJ |
| description | Abstract Solid electrolytes with fast ion transport are crucial for solid state lithium metal batteries. Chemical doping has been the most effective strategy for improving ion condictiviy, and atomistic simulation with machine-learning potentials helps optimize doping by predicting ion conductivity for various composition. Yet most existing machine-learning models are trained on narrow chemistry, requiring retraining for each new system, which wastes transferable knowledge and incurs significant cost. Here, we propose a pre-trained deep potential model purpose-built for sulfide solid electrolytes with attention mechanism, known as DPA-SSE. The training set includes 15 elements and consists of both equilibrium and extensive out-of-equilibrium configurations. DPA-SSE achieves a high energy resolution of less than 2 meV/atom for dynamical trajectories up to 1150 K, and reproduces experimental ion conductivity with remarkable accuracy. DPA-SSE generalizes well to complex electrolytes with mixes of cation and anion atoms, and enables highly efficient dynamical simulation via model distillation. DPA-SSE also serves as a platform for continuous learning and can be fine-tuned with minimal downstream data. These results demonstrate the possibility of a new pathway for the AI-driven development of solid electrolytes with exceptional performance. |
| format | Article |
| id | doaj-art-347e285fc59f4f45963da8d3851ebd78 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-347e285fc59f4f45963da8d3851ebd782025-08-24T11:40:13ZengNature Portfolionpj Computational Materials2057-39602025-08-0111111010.1038/s41524-025-01764-6A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracyRuoyu Wang0Mingyu Guo1Yuxiang Gao2Xiaoxu Wang3Yuzhi Zhang4Bin Deng5Mengchao Shi6Linfeng Zhang7Zhicheng Zhong8School of Artificial Intelligence and Data Science, University of Science and Technology of ChinaDP TechnologySchool of Artificial Intelligence and Data Science, University of Science and Technology of ChinaDP TechnologyDP TechnologyDP TechnologySuzhou LabDP TechnologySchool of Artificial Intelligence and Data Science, University of Science and Technology of ChinaAbstract Solid electrolytes with fast ion transport are crucial for solid state lithium metal batteries. Chemical doping has been the most effective strategy for improving ion condictiviy, and atomistic simulation with machine-learning potentials helps optimize doping by predicting ion conductivity for various composition. Yet most existing machine-learning models are trained on narrow chemistry, requiring retraining for each new system, which wastes transferable knowledge and incurs significant cost. Here, we propose a pre-trained deep potential model purpose-built for sulfide solid electrolytes with attention mechanism, known as DPA-SSE. The training set includes 15 elements and consists of both equilibrium and extensive out-of-equilibrium configurations. DPA-SSE achieves a high energy resolution of less than 2 meV/atom for dynamical trajectories up to 1150 K, and reproduces experimental ion conductivity with remarkable accuracy. DPA-SSE generalizes well to complex electrolytes with mixes of cation and anion atoms, and enables highly efficient dynamical simulation via model distillation. DPA-SSE also serves as a platform for continuous learning and can be fine-tuned with minimal downstream data. These results demonstrate the possibility of a new pathway for the AI-driven development of solid electrolytes with exceptional performance.https://doi.org/10.1038/s41524-025-01764-6 |
| spellingShingle | Ruoyu Wang Mingyu Guo Yuxiang Gao Xiaoxu Wang Yuzhi Zhang Bin Deng Mengchao Shi Linfeng Zhang Zhicheng Zhong A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy npj Computational Materials |
| title | A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy |
| title_full | A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy |
| title_fullStr | A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy |
| title_full_unstemmed | A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy |
| title_short | A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy |
| title_sort | pre trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy |
| url | https://doi.org/10.1038/s41524-025-01764-6 |
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