Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals
Assessing catalytic mechanisms using DFT calculations greatly aids catalyst design, but is impractical for large molecules. Here the authors develop a statistical learning-based thermochemical model for estimating adsorption of organics onto metals, retaining DFT accuracy while reducing the number o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2019-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-12709-1 |