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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17847 |
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| _version_ | 1866908786625609728 |
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| author | Liu, Xiaoqing Yu, Xinyu Wang, Yangshuai Sun, Zhe-Tao Luo, Zedong Zeng, Kehan Zhao, Teng Bo, Shou-Hang Xu, Zhenli |
| author_facet | Liu, Xiaoqing Yu, Xinyu Wang, Yangshuai Sun, Zhe-Tao Luo, Zedong Zeng, Kehan Zhao, Teng Bo, Shou-Hang Xu, Zhenli |
| contents | Atomistic modeling of solid-solid battery interfaces is essential for understanding electro-chemo-mechanical coupling, but the complex interfacial chemistry and heterogeneous environments pose major challenges for quantum-accurate, data-efficient modeling. Herein, we propose an approach of fine-tuning with integrated replay and efficiency (FIRE), a general framework for universal machine-learning interatomic potentials by combining efficient configurational sampling with a replay-argumented continual strategy, achieving quantum-level accuracy at moderate cost. Across six solid-solid battery interface systems, FIRE consistently achieves root-mean-square errors in energy below 1 meV/atom and in force near 20 meV/angstrom, marking an order-of-magnitude improvement over existing models while requiring only 10% of the original datasets. In addition, the fine-tuned model successfully reproduces key mechanical and electrochemical properties of the materials, in close agreement with experimental data. The FIRE offers a generalizable and data-efficient approach for developing accurate interatomic potentials across diverse materials, enabling predictive simulations beyond the reach of first-principles methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17847 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces Liu, Xiaoqing Yu, Xinyu Wang, Yangshuai Sun, Zhe-Tao Luo, Zedong Zeng, Kehan Zhao, Teng Bo, Shou-Hang Xu, Zhenli Materials Science Atomistic modeling of solid-solid battery interfaces is essential for understanding electro-chemo-mechanical coupling, but the complex interfacial chemistry and heterogeneous environments pose major challenges for quantum-accurate, data-efficient modeling. Herein, we propose an approach of fine-tuning with integrated replay and efficiency (FIRE), a general framework for universal machine-learning interatomic potentials by combining efficient configurational sampling with a replay-argumented continual strategy, achieving quantum-level accuracy at moderate cost. Across six solid-solid battery interface systems, FIRE consistently achieves root-mean-square errors in energy below 1 meV/atom and in force near 20 meV/angstrom, marking an order-of-magnitude improvement over existing models while requiring only 10% of the original datasets. In addition, the fine-tuned model successfully reproduces key mechanical and electrochemical properties of the materials, in close agreement with experimental data. The FIRE offers a generalizable and data-efficient approach for developing accurate interatomic potentials across diverse materials, enabling predictive simulations beyond the reach of first-principles methods. |
| title | An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces |
| topic | Materials Science |
| url | https://arxiv.org/abs/2601.17847 |