| Autor: |
Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Communications Materials, Vol 3, Iss 1, Pp 1-18 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2662-4443 |
| DOI: |
10.1038/s43246-022-00315-6 |
| Popis: |
Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and chemistry, indicating a possible road-map for their further development. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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