https://scholars.lib.ntu.edu.tw/handle/123456789/611454
標題: | Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties | 作者: | Cho E.H. Lin L.-C. LI-CHIANG LIN |
關鍵字: | Adsorption;Convolution;Forecasting;Image recognition;Nanopores;Plasma interactions;Porous materials;Zeolites;Adsorption properties;Data-driven approach;Energy applications;Highly accurate;Important features;Material recognition;Methane adsorption;Nano-porous materials;Convolutional neural networks | 公開日期: | 2021 | 卷: | 12 | 期: | 9 | 起(迄)頁: | 2279-2285 | 來源出版物: | Journal of Physical Chemistry Letters | 摘要: | 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. ? |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102905960&doi=10.1021%2facs.jpclett.1c00293&partnerID=40&md5=66973eaeec10a098f78fa8ea8fc4ab93 https://scholars.lib.ntu.edu.tw/handle/123456789/611454 |
DOI: | 10.1021/acs.jpclett.1c00293 |
顯示於: | 化學工程學系 |
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