Visualization and quantification of geometric diversity in metal-organic frameworks
With ever-growing numbers of metal–organic framework (MOF) materials being reported, new computational approaches are required for a quantitative understanding of structure–property correlations in MOFs. Here, we show how structural coarse-graining and embedding (“unsupervised learning”) schemes can...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
American Chemical Society
2021
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