Hydrogen uptake prediction in porous carbon materials explained by decision tree machine learning Algorithms: From experimental data to interpretable predictions
Widespread adoption of hydrogen fuel is constrained by the cost and safety limits of high-pressure and cryogenic storage. Adsorption-based storage in Porous Carbon Materials (PCMs) is a promising alternative, yet its potential is unrealized due to the research time and cost of discovery. A Machine L...
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| Veröffentlicht in: | International journal of hydrogen energy Jg. 197; S. 152704 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
05.01.2026
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| ISSN: | 0360-3199 |
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| Abstract | Widespread adoption of hydrogen fuel is constrained by the cost and safety limits of high-pressure and cryogenic storage. Adsorption-based storage in Porous Carbon Materials (PCMs) is a promising alternative, yet its potential is unrealized due to the research time and cost of discovery. A Machine Learning (ML) approach was developed using five Decision Tree-based models on a 2,101datapoint PCM dataset to rapidly address the demands of this gap. CatBoost delivered the best performance (R2 = 0.9983, RMSE = 0.094, and MAE = 0.053), outperforming the Stacking Ensemble model (improving R2 by 0.1 %, RMSE by 13 %, and MAE by 15 %). Further, SHAP analysis confirmed pressure, temperature, SBET, and pore volumes as the key predictors, aligning with adsorption theory. This ML strategy serves as an efficient pre-screening tool for accelerating PCM discovery and reducing research cost and time for safe and cost-effective hydrogen storage with higher interpretability compared to previously developed tools.
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•Five Decision Tree ML models were used to predict H2 uptake in PCMs.•Gradient Boosting models showed stronger regression and residual performance.•CatBoost gave the best results with R2 of 0.9983 and RMSE of 0.094.•SHAP analysis found SBET as the most influential morphological property.•A Stacking ensemble outperformed its individual constituent models. |
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| AbstractList | Widespread adoption of hydrogen fuel is constrained by the cost and safety limits of high-pressure and cryogenic storage. Adsorption-based storage in Porous Carbon Materials (PCMs) is a promising alternative, yet its potential is unrealized due to the research time and cost of discovery. A Machine Learning (ML) approach was developed using five Decision Tree-based models on a 2,101datapoint PCM dataset to rapidly address the demands of this gap. CatBoost delivered the best performance (R2 = 0.9983, RMSE = 0.094, and MAE = 0.053), outperforming the Stacking Ensemble model (improving R2 by 0.1 %, RMSE by 13 %, and MAE by 15 %). Further, SHAP analysis confirmed pressure, temperature, SBET, and pore volumes as the key predictors, aligning with adsorption theory. This ML strategy serves as an efficient pre-screening tool for accelerating PCM discovery and reducing research cost and time for safe and cost-effective hydrogen storage with higher interpretability compared to previously developed tools.
[Display omitted]
•Five Decision Tree ML models were used to predict H2 uptake in PCMs.•Gradient Boosting models showed stronger regression and residual performance.•CatBoost gave the best results with R2 of 0.9983 and RMSE of 0.094.•SHAP analysis found SBET as the most influential morphological property.•A Stacking ensemble outperformed its individual constituent models. |
| ArticleNumber | 152704 |
| Author | Sunkara, Hemanth R, Namitha Acharya, Sushmitha Siddiqui, Shabnam Sainath, Krishnamurthy Bhat A S, Shravani Shekar, Selva Kumar |
| Author_xml | – sequence: 1 givenname: Hemanth orcidid: 0009-0004-4000-437X surname: Sunkara fullname: Sunkara, Hemanth email: hemanthsunkara.ch24@bmsce.ac.in organization: Department of Chemical Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India, 560019 – sequence: 2 givenname: Shravani orcidid: 0009-0003-5388-2449 surname: Bhat A S fullname: Bhat A S, Shravani email: shravanibhat.ch24@bmsce.ac.in organization: Department of Chemical Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India, 560019 – sequence: 3 givenname: Namitha orcidid: 0009-0006-4591-8028 surname: R fullname: R, Namitha email: namithar.ch24@bmsce.ac.in organization: Department of Chemical Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India, 560019 – sequence: 4 givenname: Sushmitha orcidid: 0009-0007-4352-6802 surname: Acharya fullname: Acharya, Sushmitha email: sushmitha.ch22@bmsce.ac.in organization: Department of Chemical Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India, 560019 – sequence: 5 givenname: Selva Kumar orcidid: 0000-0002-3342-7161 surname: Shekar fullname: Shekar, Selva Kumar email: selva.cse@bmsce.ac.in organization: Department of Computer Science and Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India, 560019 – sequence: 6 givenname: Krishnamurthy orcidid: 0000-0003-3910-9401 surname: Sainath fullname: Sainath, Krishnamurthy email: sainath.che@bmsce.ac.in organization: Department of Chemical Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India, 560019 – sequence: 7 givenname: Shabnam orcidid: 0000-0002-2559-6088 surname: Siddiqui fullname: Siddiqui, Shabnam email: shabnamsiddiqui.che@bmsce.ac.in organization: Department of Chemical Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India, 560019 |
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