Cho E.H.Lin L.-C.LI-CHIANG LIN2022-05-242022-05-242021https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102905960&doi=10.1021%2facs.jpclett.1c00293&partnerID=40&md5=66973eaeec10a098f78fa8ea8fc4ab93https://scholars.lib.ntu.edu.tw/handle/123456789/611454Nanoporous 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. ?AdsorptionConvolutionForecastingImage recognitionNanoporesPlasma interactionsPorous materialsZeolitesAdsorption propertiesData-driven approachEnergy applicationsHighly accurateImportant featuresMaterial recognitionMethane adsorptionNano-porous materialsConvolutional neural networksjournal article10.1021/acs.jpclett.1c002932-s2.0-85102905960