Sripetdee, Tirayoot and Jitmitsumphan, Sorrasit and Chaimuengchuen, Tharathep and Burana-Amnuay, Makkawan and Chinkanjanarot, Sorayot and Jonglertjunya, Woranart and Ling, Tau Chuan and Phadungbut, Poomiwat (2022) Extreme gradient boosting machine for modeling hydrogen gas storage in carbon slit pores from molecular simulation data. Energy Reports, 8 (16). pp. 16-21. ISSN 2352-4847, DOI https://doi.org/10.1016/j.egyr.2022.10.229.
Full text not available from this repository.Abstract
To accelerate the computation of hydrogen storage capacity in uniform slit-shaped porous carbon determined by molecular simulation, the traditional Gradient Boosting and XGBoost algorithms are introduced to create the predictive models, and evaluate their prediction effectiveness and accuracy. The resultant models are tuned their hyperparameters by the random grid search method. From the comparison among the obtained models, it is found that the XGBoost model with optimized hyperparameters shows superior performance in predicting the hydrogen storage capacity in simulated carbon pores. According to the comparison of the results, the XGBoost model with optimized hyperparameters outperforms the other models in forecasting hydrogen storage capacity in simulated carbon pores as a function of pressure and pore sizes. Furthermore, the predicted results are in the best agreement with the pristine target dataset as measured by various evaluation metrics. Note that other models yield reasonable performance metrics, but they are unable to forecast high-pressure storage capacity in the ultramicropore region (less than 1 nm). The developed model could be applied for precisely and rapidly searching and comprehending the temperature-dependent optimal pore size for high-capacity hydrogen-storage systems in vehicular applications. (C) 2022 The Author(s). Published by Elsevier Ltd.
| Item Type: | Article |
|---|---|
| Funders: | Mahidol University, Thailand, SATU Joint Research Scheme Program 2021, Thailand |
| Additional Information: | 7th International Conference on Advances on Clean Energy Research (ICACER), Barcelona, SPAIN, APR 20-22, 2022 |
| Uncontrolled Keywords: | Hydrogen storage; Data prediction; Gradient Boosting; XGBoost; Molecular simulation |
| Subjects: | T Technology > TP Chemical technology |
| Divisions: | Faculty of Science > Institute of Biological Sciences |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Nov 2023 07:36 |
| Last Modified: | 22 Nov 2023 07:36 |
| URI: | http://eprints.um.edu.my/id/eprint/40378 |
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