A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts
Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal predictio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-11-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-020-00447-8 |