Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
Uncertainty estimations for machine learning interatomic potentials (MLIPs) are crucial for quantifying model error and identifying informative training samples in active learning (AL) strategies. In this study, we evaluate uncertainty estimations of Gaussian process regression (GPR)-based MLIPs, in...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Assuntos: | |
| Acceso en liña: | https://doi.org/10.1088/2632-2153/ae09ef |
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