Uncertainty quantification in graph neural networks with shallow ensembles
Machine-learned potentials (MLPs) have revolutionized materials discovery by providing accurate and efficient predictions of molecular and material properties. Graph neural networks (GNNs) have emerged as a state-of-the-art approach due to their ability to capture complex atomic interactions. Howeve...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
IOP Publishing
2025-01-01
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| Serie: | Machine Learning: Science and Technology |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1088/2632-2153/ae0bf0 |
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