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Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descri...
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| Опубликовано в: : | Sci Rep |
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| Главные авторы: | , , , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Nature Publishing Group UK
2021
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8076181/ https://ncbi.nlm.nih.gov/pubmed/33903606 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-021-88027-8 |
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