Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
Machine learning: Modelling atomic systems to make property predictions A method for representing atomic systems for machine learning is shown that can provide access to the physical properties of these systems. Machine learning is a powerful tool for finding correlations but when used to look at re...
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| Glavni autori: | , , , |
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| Format: | Artigo |
| Jezik: | Inglês |
| Izdano: |
Nature Portfolio
2017-08-01
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| Serija: | npj Computational Materials |
| Online pristup: | https://doi.org/10.1038/s41524-017-0027-x |
| Oznake: |
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