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Title: Prediction of Carbon Dioxide Adsorption via Deep Learning

Journal Article · · Angewandte Chemie (International Edition)
 [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)
+ Show Author Affiliations

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.

Research Organization:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1486930
Alternate ID(s):
OSTI ID: 1489089
Journal Information:
Angewandte Chemie (International Edition), Vol. 130, Issue n/a; ISSN 1433-7851
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 58 works
Citation information provided by
Web of Science

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Cited By (2)

Inverse design of porous materials using artificial neural networks journal January 2020
Simultaneous Conversion of C 5 and C 6 Sugars into Methyl Levulinate with the Addition of 1,3,5‐Trioxane journal September 2019

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Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
CO2 adsorption
machine learning
porous carbon
textural properties