Applied machine learning to analyze and predict CO2 adsorption behavior of metal-organic frameworks

Machine learning provides new insights for designing MOFs with high CO2 adsorption capacity and understanding the CO2 adsorption mechanism. In this work, 348 data points from published reports were collected and four tree-based models were designed to predict the CO2 adsorption capacity of MOFs by m...

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Bibliographic Details
Published inCarbon Capture Science & Technology Vol. 9; p. 100146
Main Authors Li, Xiaoqiang, Zhang, Xiong, Zhang, Junjie, Gu, Jinyang, Zhang, Shibiao, Li, Guangyang, Shao, Jingai, He, Yong, Yang, Haiping, Zhang, Shihong, Chen, Hanping
Format Journal Article
LanguageEnglish
Published Elsevier 01.12.2023
Subjects
CO2 adsorption
Features analysis
Machine learning
MOFs
Random forest
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Summary:Machine learning provides new insights for designing MOFs with high CO2 adsorption capacity and understanding the CO2 adsorption mechanism. In this work, 348 data points from published reports were collected and four tree-based models were designed to predict the CO2 adsorption capacity of MOFs by machine learning. The results showed that the Random Forest (RF) had the best prediction performance (R2train = 0.970, R2test = 0.896). Feature importance analysis revealed the relative importance of CO2 adsorption parameters (73 %), textures (23 %) and metal centers of MOFs (4 %) for the CO2 adsorption process. Single and synergistic effects of different features were observed through partial dependence analysis. MOFs with Cu, Fe, Co, and Ni metal centers exhibited a promoting effect on CO2 adsorption. In addition, under high pressure, well-developed textures had significant positive impact on CO2 adsorption capacity, while under medium and low pressure, textures were not determining factors.
ISSN:2772-6568
2772-6568
DOI:10.1016/j.ccst.2023.100146