Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Preparation
2.2. Deep Learning Algorithms
2.3. Model Development
2.4. Model Evaluation
3. Results and Discussion
3.1. Prediction of Methane Adsorption of MOFs
3.2. Prediction of Carbon Dioxide Adsorption of MOFs
3.3. Model Transferability
3.4. Models Constructed Using a Mixture of MOFs and COFs
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Disclaimer
References
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Gas | Pressure (bar) | MOFs | Mean Adsorption | Standard Deviation |
---|---|---|---|---|
CO2 | 0.05 | 70,433 | 2.2466 | 5.3255 |
CO2 | 0.5 | 70,433 | 37.365 | 35.3544 |
CO2 | 2.5 | 70,433 | 92.9512 | 56.5359 |
CH4 | 1 | 70,608 | 17.8184 | 17.7097 |
CH4 | 5.8 | 28,417 | 57.5549 | 33.442 |
CH4 | 35 | 70,605 | 139.1942 | 44.8701 |
CH4 | 65 | 27,151 | 172.11 | 50.1996 |
CH4 | 65 | 17,098 * | 153.3413 | 37.7033 |
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Guo, W.; Liu, J.; Dong, F.; Chen, R.; Das, J.; Ge, W.; Xu, X.; Hong, H. Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials. Nanomaterials 2022, 12, 3376. https://doi.org/10.3390/nano12193376
Guo W, Liu J, Dong F, Chen R, Das J, Ge W, Xu X, Hong H. Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials. Nanomaterials. 2022; 12(19):3376. https://doi.org/10.3390/nano12193376
Chicago/Turabian StyleGuo, Wenjing, Jie Liu, Fan Dong, Ru Chen, Jayanti Das, Weigong Ge, Xiaoming Xu, and Huixiao Hong. 2022. "Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials" Nanomaterials 12, no. 19: 3376. https://doi.org/10.3390/nano12193376
APA StyleGuo, W., Liu, J., Dong, F., Chen, R., Das, J., Ge, W., Xu, X., & Hong, H. (2022). Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials. Nanomaterials, 12(19), 3376. https://doi.org/10.3390/nano12193376