Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach
<p dir="ltr">Hydrogen is a promising alternative energy source due to its significantly high energy density. Also, hydrogen can be transformed into electricity in energy systems such as fuel cells. The transition toward hydrogen-consuming applications requires a hydrogen storage meth...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
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| _version_ | 1864513531625340928 |
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| author | Majedeh Gheytanzadeh (17541927) |
| author2 | Fatemeh Rajabhasani (17541930) Alireza Baghban (5159648) Sajjad Habibzadeh (5548580) Otman Abida (2071714) Amin Esmaeili (17541204) Muhammad Tajammal Munir (17541933) |
| author2_role | author author author author author author |
| author_facet | Majedeh Gheytanzadeh (17541927) Fatemeh Rajabhasani (17541930) Alireza Baghban (5159648) Sajjad Habibzadeh (5548580) Otman Abida (2071714) Amin Esmaeili (17541204) Muhammad Tajammal Munir (17541933) |
| author_role | author |
| dc.creator.none.fl_str_mv | Majedeh Gheytanzadeh (17541927) Fatemeh Rajabhasani (17541930) Alireza Baghban (5159648) Sajjad Habibzadeh (5548580) Otman Abida (2071714) Amin Esmaeili (17541204) Muhammad Tajammal Munir (17541933) |
| dc.date.none.fl_str_mv | 2022-12-19T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-022-26522-2 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Estimating_hydrogen_absorption_energy_on_different_metal_hydrides_using_Gaussian_process_regression_approach/24717465 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Chemical sciences Theoretical and computational chemistry Engineering Electrical engineering Materials engineering Information and computing sciences Machine learning hydrogen energy metal hydrides alternative energy source |
| dc.title.none.fl_str_mv | Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Hydrogen is a promising alternative energy source due to its significantly high energy density. Also, hydrogen can be transformed into electricity in energy systems such as fuel cells. The transition toward hydrogen-consuming applications requires a hydrogen storage method that comes with pack hydrogen with high density. Among diverse methods, absorbing hydrogen on host metal is applicable at room temperature and pressure, which does not provide any safety concerns. In this regard, AB<sub>2</sub> metal hydride with potentially high hydrogen density is selected as an appropriate host. Machine learning techniques have been applied to establish a relationship on the effect of the chemical composition of these hosts on hydrogen storage. For this purpose, a data bank of 314 data point pairs was used. In this assessment, the different A-site and B-site elements were used as the input variables, while the hydrogen absorption energy resulted in the output. A robust Gaussian process regression (GPR) approach with four kernel functions is proposed to predict the hydrogen absorption energy based on the inputs. All the GPR models' performance was quite excellent; notably, GPR with Exponential kernel function showed the highest preciseness with R<sup>2</sup>, MRE, MSE, RMSE, and STD of 0.969, 2.291%, 3.909, 2.501, and 1.878, respectively. Additionally, the sensitivity of analysis indicated that ZR, Ti, and Cr are the most demining elements in this system.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-022-26522-2" target="_blank">https://dx.doi.org/10.1038/s41598-022-26522-2</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ee9873421ac0d59dc3863ddb51f54c66 |
| identifier_str_mv | 10.1038/s41598-022-26522-2 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717465 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approachMajedeh Gheytanzadeh (17541927)Fatemeh Rajabhasani (17541930)Alireza Baghban (5159648)Sajjad Habibzadeh (5548580)Otman Abida (2071714)Amin Esmaeili (17541204)Muhammad Tajammal Munir (17541933)Chemical sciencesTheoretical and computational chemistryEngineeringElectrical engineeringMaterials engineeringInformation and computing sciencesMachine learninghydrogenenergymetal hydridesalternative energy source<p dir="ltr">Hydrogen is a promising alternative energy source due to its significantly high energy density. Also, hydrogen can be transformed into electricity in energy systems such as fuel cells. The transition toward hydrogen-consuming applications requires a hydrogen storage method that comes with pack hydrogen with high density. Among diverse methods, absorbing hydrogen on host metal is applicable at room temperature and pressure, which does not provide any safety concerns. In this regard, AB<sub>2</sub> metal hydride with potentially high hydrogen density is selected as an appropriate host. Machine learning techniques have been applied to establish a relationship on the effect of the chemical composition of these hosts on hydrogen storage. For this purpose, a data bank of 314 data point pairs was used. In this assessment, the different A-site and B-site elements were used as the input variables, while the hydrogen absorption energy resulted in the output. A robust Gaussian process regression (GPR) approach with four kernel functions is proposed to predict the hydrogen absorption energy based on the inputs. All the GPR models' performance was quite excellent; notably, GPR with Exponential kernel function showed the highest preciseness with R<sup>2</sup>, MRE, MSE, RMSE, and STD of 0.969, 2.291%, 3.909, 2.501, and 1.878, respectively. Additionally, the sensitivity of analysis indicated that ZR, Ti, and Cr are the most demining elements in this system.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-022-26522-2" target="_blank">https://dx.doi.org/10.1038/s41598-022-26522-2</a></p><p dir="ltr">Disclaimer: The University of Doha for Science and Technology replaced the now-former College of the North Atlantic-Qatar after an Amiri decision in 2022. UDST has become and first national applied University in Qatar; it is also second national University in the country.</p>2022-12-19T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-022-26522-2https://figshare.com/articles/journal_contribution/Estimating_hydrogen_absorption_energy_on_different_metal_hydrides_using_Gaussian_process_regression_approach/24717465CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247174652022-12-19T03:00:00Z |
| spellingShingle | Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach Majedeh Gheytanzadeh (17541927) Chemical sciences Theoretical and computational chemistry Engineering Electrical engineering Materials engineering Information and computing sciences Machine learning hydrogen energy metal hydrides alternative energy source |
| status_str | publishedVersion |
| title | Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach |
| title_full | Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach |
| title_fullStr | Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach |
| title_full_unstemmed | Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach |
| title_short | Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach |
| title_sort | Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach |
| topic | Chemical sciences Theoretical and computational chemistry Engineering Electrical engineering Materials engineering Information and computing sciences Machine learning hydrogen energy metal hydrides alternative energy source |