Deep learning neural network potential for simulating gaseous adsorption in metal–organic frameworks
| Autor: | Chi-Ta Yang, Ishan Pandey, Dan Trinh, Chau-Chyun Chen, Joshua D. Howe, Li-Chiang Lin |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Materials Advances. 3:5299-5303 |
| ISSN: | 2633-5409 |
| Popis: | This study proposes ab initio neural network force fields with physically motivated features to offer superior accuracy in describing adsorbate–adsorbent interactions of nonpolar (CO2) and polar (H2O and CO) molecules in metal–organic frameworks with open-metal sites. |
| Databáze: | OpenAIRE |
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