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Machine learned features from density of states for accurate adsorption energy prediction
Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence the...
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| Publicado no: | Nat Commun |
|---|---|
| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2021
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7782579/ https://ncbi.nlm.nih.gov/pubmed/33398014 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-020-20342-6 |
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