Prediction of Carbon Dioxide Adsorption via Deep Learning

• Published in: Angewandte Chemie International Edition Vol. 58; no. 1; pp. 259 - 263
• Main Authors: Zhang, Zihao, Schott, Jennifer A., Liu, Miaomiao, Chen, Hao, Lu, Xiuyang, Sumpter, Bobby G., Fu, Jie, Dai, Sheng
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 02-01-2019
• Edition: International ed. in English
• Subjects: Adsorption; Artificial neural networks; Carbon dioxide; CO2 adsorption; Deep learning; Learning algorithms; Machine learning; Mathematical models; Neural networks; porous carbon; Predictions; Properties (attributes); textural properties; porous carbon; machine learning; CO2 adsorption; textural properties
• ISSN: 1433-7851; 1521-3773
• DOI: 10.1002/anie.201812363

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.
Learn on me: The artificial neural network, a type of machine learning algorithm, is used to model the relationship between CO2 adsorption capacity and the textural properties of porous carbon. Then, it is used as an implicit model to precisely predict the CO2 adsorption capacity of unknow porous carbons based on its textural properties.