Neural network potentials with effective charge separation for non-equilibrium dynamics of ionic solids: a ZnO case study
Abstract Developing neural network potentials (NNPs) accurate under non-equilibrium dynamics is challenging, as such systems require extensive sampling beyond equilibrium phases. Here we construct high-fidelity NNPs for zinc oxide (ZnO), a polymorphic ionic solid, using density functional theory (DF...
Gardado en:
| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
Nature Portfolio
2026-01-01
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| Series: | npj Computational Materials |
| Acceso en liña: | https://doi.org/10.1038/s41524-025-01946-2 |
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