A Perspective on Training Machine Learning Force Fields for Solid-State Electrolyte Materials
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912951859937280 |
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| author | Yan, Zihan Tang, Shengjie Zhu, Yizhou |
| author_facet | Yan, Zihan Tang, Shengjie Zhu, Yizhou |
| contents | Machine learning force fields enable high-accuracy modeling of solid-state electrolytes (SSEs). This perspective evaluates dataset size, reference quality, and model architectures. We show that rigid SSE frameworks favor efficient learning, prioritizing data quality over quantity. Crucially, force RMSE does not reliably predict transport performance. By analyzing locality and benchmarking frameworks, we provide practical guidelines to accelerate the development of next-generation solid-state batteries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07425 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Perspective on Training Machine Learning Force Fields for Solid-State Electrolyte Materials Yan, Zihan Tang, Shengjie Zhu, Yizhou Materials Science Machine learning force fields enable high-accuracy modeling of solid-state electrolytes (SSEs). This perspective evaluates dataset size, reference quality, and model architectures. We show that rigid SSE frameworks favor efficient learning, prioritizing data quality over quantity. Crucially, force RMSE does not reliably predict transport performance. By analyzing locality and benchmarking frameworks, we provide practical guidelines to accelerate the development of next-generation solid-state batteries. |
| title | A Perspective on Training Machine Learning Force Fields for Solid-State Electrolyte Materials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2603.07425 |