https://scholars.lib.ntu.edu.tw/handle/123456789/611454
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho E.H. | en_US |
dc.contributor.author | Lin L.-C. | en_US |
dc.contributor.author | LI-CHIANG LIN | en_US |
dc.creator | Cho E.H.;Lin L.-C. | - |
dc.date.accessioned | 2022-05-24T06:10:30Z | - |
dc.date.available | 2022-05-24T06:10:30Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102905960&doi=10.1021%2facs.jpclett.1c00293&partnerID=40&md5=66973eaeec10a098f78fa8ea8fc4ab93 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/611454 | - |
dc.description.abstract | Nanoporous materials can be effective adsorbents for various energy applications. Because of their abundant number, brute-force-based material discovery can, however, be challenging. Data-driven approaches can be advantageous for such purposes. In this study, we demonstrate for the first time the applicability of a 3D convolutional neural network (CNN) in material recognition for predicting adsorption properties. 2D CNNs have been widely applied to image recognition, where the CNN self-learns important features of images, without the need of handcrafting features that are subject to human bias. This study explores methane adsorption in zeolites as a case study, where ?6500 hypothetical zeolites are utilized to train/validate our designed CNN model. The CNN model offers highly accurate predictions, and the self-learned features resemble the channel and pore-like geometry of structures. This study demonstrates the extension of computer vision to materials science and paves the way for future studies such as carbon capture. ? | - |
dc.relation.ispartof | Journal of Physical Chemistry Letters | - |
dc.subject | Adsorption | - |
dc.subject | Convolution | - |
dc.subject | Forecasting | - |
dc.subject | Image recognition | - |
dc.subject | Nanopores | - |
dc.subject | Plasma interactions | - |
dc.subject | Porous materials | - |
dc.subject | Zeolites | - |
dc.subject | Adsorption properties | - |
dc.subject | Data-driven approach | - |
dc.subject | Energy applications | - |
dc.subject | Highly accurate | - |
dc.subject | Important features | - |
dc.subject | Material recognition | - |
dc.subject | Methane adsorption | - |
dc.subject | Nano-porous materials | - |
dc.subject | Convolutional neural networks | - |
dc.title | Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties | en_US |
dc.type | journal article | en |
dc.identifier.doi | 10.1021/acs.jpclett.1c00293 | - |
dc.identifier.scopus | 2-s2.0-85102905960 | - |
dc.relation.pages | 2279-2285 | - |
dc.relation.journalvolume | 12 | - |
dc.relation.journalissue | 9 | - |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | journal article | - |
item.grantfulltext | none | - |
crisitem.author.dept | Chemical Engineering | - |
crisitem.author.parentorg | College of Engineering | - |
Appears in Collections: | 化學工程學系 |
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