Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d-band model to capture the essential physics of adsorbate–subst...
| Autores principales: | , , |
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| Formato: | Artículo |
| Lenguaje: | English |
| Publicado: |
Nature Portfolio
2020-11-01
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| Colección: | Nature Communications |
| Acceso en línea: | https://doi.org/10.1038/s41467-020-19524-z |