The good, the bad, and the ugly of atomistic learning for ‘clusters-to-bulk’ generalization
Training machine learning interatomic potentials (MLIPs) on total energies of molecular clusters using differential or transfer learning is becoming a popular route to extend the accuracy of correlated wave-function theory to condensed phases. A key challenge, however, lies in validation, as referen...
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| Autors principals: | , , , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
IOP Publishing
2026-01-01
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| Col·lecció: | Machine Learning: Science and Technology |
| Matèries: | |
| Accés en línia: | https://doi.org/10.1088/2632-2153/ae3c57 |
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