Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields
Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surf...
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| Principais autores: | , , , |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
IOP Publishing
2026-01-01
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| Serier: | Machine Learning: Science and Technology |
| Fag: | |
| Online adgang: | https://doi.org/10.1088/2632-2153/ae39a1 |
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