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Title: Prediction of Carbon Dioxide Adsorption via Deep Learning

Abstract

Porous carbons with different textural properties exhibit great differences in CO2 adsorption capacity. It is generally known that narrow micropores contribute to higher CO2 adsorption capacity. However, it is still unclear what role each variable in the textural properties plays in CO2 adsorption. Herein, a deep neural network is trained as a generative model to direct the relationship between CO2 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 CO2 adsorption capacity of unknown porous carbons. Interestingly, the practical CO2 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.

Authors:
 [1];  [2];  [3];  [4];  [4];  [5];  [4]; ORCiD logo [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States); Zhejiang Univ., Hangzhou (China)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  3. Univ. of Tennessee, Knoxville, TN (United States)
  4. Zhejiang Univ., Hangzhou (China)
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1486930
Alternate Identifier(s):
OSTI ID: 1489089
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Angewandte Chemie (International Edition)
Additional Journal Information:
Journal Name: Angewandte Chemie (International Edition); Journal Volume: 130; Journal Issue: n/a; Journal ID: ISSN 1433-7851
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; CO2 adsorption; machine learning; porous carbon; textural properties

Citation Formats

Zhang, Zihao, Schott, Jennifer A., Liu, Miaomiao, Chen, Hao, Lu, Xiuyang, Sumpter, Bobby G., Fu, Jie, and Dai, Sheng. Prediction of Carbon Dioxide Adsorption via Deep Learning. United States: N. p., 2018. Web. doi:10.1002/anie.201812363.
Zhang, Zihao, Schott, Jennifer A., Liu, Miaomiao, Chen, Hao, Lu, Xiuyang, Sumpter, Bobby G., Fu, Jie, & Dai, Sheng. Prediction of Carbon Dioxide Adsorption via Deep Learning. United States. doi:10.1002/anie.201812363.
Zhang, Zihao, Schott, Jennifer A., Liu, Miaomiao, Chen, Hao, Lu, Xiuyang, Sumpter, Bobby G., Fu, Jie, and Dai, Sheng. Tue . "Prediction of Carbon Dioxide Adsorption via Deep Learning". United States. doi:10.1002/anie.201812363. https://www.osti.gov/servlets/purl/1486930.
@article{osti_1486930,
title = {Prediction of Carbon Dioxide Adsorption via Deep Learning},
author = {Zhang, Zihao and Schott, Jennifer A. and Liu, Miaomiao and Chen, Hao and Lu, Xiuyang and Sumpter, Bobby G. and Fu, Jie and Dai, Sheng},
abstractNote = {Porous carbons with different textural properties exhibit great differences in CO2 adsorption capacity. It is generally known that narrow micropores contribute to higher CO2 adsorption capacity. However, it is still unclear what role each variable in the textural properties plays in CO2 adsorption. Herein, a deep neural network is trained as a generative model to direct the relationship between CO2 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 CO2 adsorption capacity of unknown porous carbons. Interestingly, the practical CO2 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.},
doi = {10.1002/anie.201812363},
journal = {Angewandte Chemie (International Edition)},
number = n/a,
volume = 130,
place = {United States},
year = {2018},
month = {12}
}

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