Machine learned features from density of states for accurate adsorption energy prediction
Computational catalysis would strongly benefit from general descriptors applicable for predicting adsorption energetics. Here the authors propose a machine-learning approach for adsorption energy predictions based on learning the relevant descriptors in a surface atom's density of states as par...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-20342-6 |