Exploiting redundancy in large materials datasets for efficient machine learning with less data
Abstract Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95% of data can be safely removed fr...
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| Principais autores: | , , , , , |
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| פורמט: | Artigo |
| שפה: | Inglês |
| יצא לאור: |
Nature Portfolio
2023-11-01
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| סדרה: | Nature Communications |
| גישה מקוונת: | https://doi.org/10.1038/s41467-023-42992-y |
| תגים: |
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