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Title: Prediction of Carbon Dioxide Adsorption via Deep Learning

Journal Article · · Angewandte Chemie
 [1];  [2];  [3];  [4];  [4];  [5];  [4]; ORCiD logo [2]
  1. Key Laboratory of Biomass Chemical Engineering of Ministry of Education College of Chemical and Biological Engineering Zhejiang University Hangzhou 310027 China, Chemical Sciences Division Oak Ridge National Laboratory Oak Ridge TN USA, Department of Chemistry University of Tennessee Knoxville TN USA
  2. Chemical Sciences Division Oak Ridge National Laboratory Oak Ridge TN USA, Department of Chemistry University of Tennessee Knoxville TN USA
  3. Department of Chemistry University of Tennessee Knoxville TN USA
  4. Key Laboratory of Biomass Chemical Engineering of Ministry of Education College of Chemical and Biological Engineering Zhejiang University Hangzhou 310027 China
  5. Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge TN USA
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Abstract Porous carbons with different textural properties exhibit great differences in CO 2 adsorption capacity. It is generally known that narrow micropores contribute to higher CO 2 adsorption capacity. However, it is still unclear what role each variable in the textural properties plays in CO 2 adsorption. Herein, a deep neural network is trained as a generative model to direct the relationship between CO 2 adsorption of porous carbons and corresponding textural properties. The trained neural network is further employed as an implicit model to estimate its ability to predict the CO 2 adsorption capacity of unknown porous carbons. Interestingly, the practical CO 2 adsorption amounts are in good agreement with predicted values using surface area, micropore and mesopore volumes as the input values simultaneously. This unprecedented deep learning neural network (DNN) approach, a type of machine learning algorithm, exhibits great potential to predict gas adsorption and guide the development of next‐generation carbons.

Sponsoring Organization:
USDOE
OSTI ID:
1785888
Journal Information:
Angewandte Chemie, Journal Name: Angewandte Chemie Vol. 131 Journal Issue: 1; ISSN 0044-8249
Publisher:
Wiley Blackwell (John Wiley & Sons)Copyright Statement
Country of Publication:
Germany
Language:
English

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