| Autor: |
Shih-Han Wang, Hemanth Somarajan Pillai, Siwen Wang, Luke E. K. Achenie, Hongliang Xin |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-021-25639-8 |
| Popis: |
Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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