Machine-Learning-Based Prediction of Methane Adsorption Isotherms at Varied Temperatures for Experimental Adsorbents
| Autor: | Youn Sang Bae, Seo Yul Kim, Seung Ik Kim |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Materials science
Crystalline materials Thermodynamics 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences Methane 0104 chemical sciences Surfaces Coatings and Films Electronic Optical and Magnetic Materials chemistry.chemical_compound General Energy Adsorption chemistry Physical and Theoretical Chemistry 0210 nano-technology Grand canonical monte carlo |
| Zdroj: | The Journal of Physical Chemistry C. 124:19538-19547 |
| ISSN: | 1932-7455 1932-7447 |
| DOI: | 10.1021/acs.jpcc.0c01757 |
| Popis: | Metal–organic frameworks (MOFs) are crystalline materials and one of the optimal materials for large-scale grand canonical Monte Carlo (GCMC) simulations. Recently, there have been trials for apply... |
| Databáze: | OpenAIRE |
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