Machine learned interatomic potentials for ternary carbides trained on the AFLOW database
Abstract Large-density functional theory (DFT) databases are a treasure trove of energies, forces, and stresses that can be used to train machine-learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database to train moment tensor potentials...
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| Principais autores: | , , , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Portfolio
2024-07-01
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| coleção: | npj Computational Materials |
| Acesso em linha: | https://doi.org/10.1038/s41524-024-01321-7 |
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