https://scholars.lib.ntu.edu.tw/handle/123456789/598146
標題: | Chemistry-Encoded Convolutional Neural Networks for Predicting Gaseous Adsorption in Porous Materials | 作者: | Hung T.-H Xu Z.-X Kang D.-Y Lin L.-C. DUN-YEN KANG |
關鍵字: | Convolution;Convolutional neural networks;Forecasting;Gas adsorption;Network coding;Porous materials;Zeolites;Adsorption properties;Adsorption selectivity;Atomic locations;Chemical information;Convolutional neural network;Framework structures;Gaseous adsorption;Lennard-Jones parameters;Metalorganic frameworks (MOFs);Neural network model;Digital storage | 公開日期: | 2021 | 來源出版物: | Journal of Physical Chemistry C | 摘要: | Metal–organic frameworks (MOFs) are an emerging class of materials possessing significant potential in separation and storage applications. Identifying optimal candidates from tens of thousands of MOFs that have been reported is a challenging task. To this end, machine learning (ML) represents a promising approach to facilitate the selection of best-performing MOFs. In this study, we propose a scheme to develop chemistry-encoded convolutional neural network (CNN) models to predict gaseous adsorption properties, i.e., Henry’s constants of adsorption and adsorption selectivity, in chemically diverse MOFs. To train CNN models, the MOF structures are represented by their atomic locations coupled with associated chemical information of each framework atom including the 6–12 Lennard-Jones parameters (i.e., σ and ?) and point-charge values (i.e., q). Henry’s constants of CH4 and CO2 in approximately 10 000 MOF structures computed via molecular simulations are used for training and testing. Our developed CNN models show a superior prediction accuracy. Models for zeolites are also developed for comparative purposes. Various key aspects of the CNN models, such as data augmentation and spatial resolution, are also systematically investigated for achieving high accuracy. ? 2022 American Chemical Society |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124284096&doi=10.1021%2facs.jpcc.1c09649&partnerID=40&md5=1101deb19a474724350a6454f1ec7ee3 https://scholars.lib.ntu.edu.tw/handle/123456789/598146 |
ISSN: | 19327447 | DOI: | 10.1021/acs.jpcc.1c09649 |
顯示於: | 化學工程學系 |
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