Deep learning neural network potential for simulating gaseous adsorption in metal-organic frameworks

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dc.creatorYang, Chi Ta
dc.creatorPandey, Ishan (TTU)
dc.creatorTrinh, Dan (TTU)
dc.creatorChen, Chau Chyun (TTU)
dc.creatorHowe, Joshua D. (TTU)
dc.creatorLin, Li Chiang
dc.date.accessioned2023-04-12T19:42:48Z
dc.date.available2023-04-12T19:42:48Z
dc.date.issued2022
dc.description© 2022 The Author(s). cc-by-nc
dc.description.abstractThis 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. Effects of the neural network architecture and features are also investigated for developing accurate models.
dc.identifier.citationYang, C.-T., Pandey, I., Trinh, D., Chen, C.-C., Howe, J.D., & Lin, L.-C.. 2022. Deep learning neural network potential for simulating gaseous adsorption in metal-organic frameworks. Materials Advances, 3(13). https://doi.org/10.1039/d1ma01152a
dc.identifier.urihttps://doi.org/10.1039/d1ma01152a
dc.identifier.urihttps://hdl.handle.net/2346/92740
dc.language.isoeng
dc.titleDeep learning neural network potential for simulating gaseous adsorption in metal-organic frameworks
dc.typeArticle

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