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...
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| Main Authors: | , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2020
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| Subjects: | |
| Online Access: | https://doaj.org/article/b19c00b76505438c92ecff26dba7d5e6 |
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