Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO
<p dir="ltr">Recently, electrochemical reduction of CO<sub>2</sub> into value-added fuels has been noticed as a promising process to decrease CO<sub>2</sub> emissions. The development of such technology is strongly depended upon tuning the surface properties o...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513531621146624 |
|---|---|
| author | Majedeh Gheytanzadeh (17541927) |
| author2 | Alireza Baghban (5159648) Sajjad Habibzadeh (5548580) Karam Jabbour (17541942) Amin Esmaeili (17541204) Amin Hamed Mashhadzadeh (17541945) Ahmad Mohaddespour (17541948) |
| author2_role | author author author author author author |
| author_facet | Majedeh Gheytanzadeh (17541927) Alireza Baghban (5159648) Sajjad Habibzadeh (5548580) Karam Jabbour (17541942) Amin Esmaeili (17541204) Amin Hamed Mashhadzadeh (17541945) Ahmad Mohaddespour (17541948) |
| author_role | author |
| dc.creator.none.fl_str_mv | Majedeh Gheytanzadeh (17541927) Alireza Baghban (5159648) Sajjad Habibzadeh (5548580) Karam Jabbour (17541942) Amin Esmaeili (17541204) Amin Hamed Mashhadzadeh (17541945) Ahmad Mohaddespour (17541948) |
| dc.date.none.fl_str_mv | 2022-12-02T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-022-25512-8 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Intelligent_route_to_design_efficient_CO_sub_2_sub_reduction_electrocatalysts_using_ANFIS_optimized_by_GA_and_PSO/24717471 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Chemical sciences Physical chemistry Theoretical and computational chemistry Engineering Materials engineering CO2 reduction electrocatalysts ANFIS GA and PSO |
| dc.title.none.fl_str_mv | Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Recently, electrochemical reduction of CO<sub>2</sub> into value-added fuels has been noticed as a promising process to decrease CO<sub>2</sub> emissions. The development of such technology is strongly depended upon tuning the surface properties of the applied electrocatalysts. Considering the high cost and time-consuming experimental investigations, computational methods, particularly machine learning algorithms, can be the appropriate approach for efficiently screening the metal alloys as the electrocatalysts. In doing so, to represent the surface properties of the electrocatalysts numerically, d-band theory-based electronic features and intrinsic properties obtained from density functional theory (DFT) calculations were used as descriptors. Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. The developed ANFIS–PSO and ANFIS–GA showed excellent performance with RMSE of 0.0411 and 0.0383, respectively, the minimum errors reported so far in this field. Additionally, the sensitivity analysis showed that the center and the filling of the d-band are the most determining parameters for the electrocatalyst surface reactivity. The present study conveniently indicates the potential and value of machine learning in directing the experimental efforts in alloy system electrocatalysts for CO<sub>2</sub> reduction.</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/s41598-022-25512-8" target="_blank">https://dx.doi.org/10.1038/s41598-022-25512-8</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_1a695cf00597d50ba9db4b74e203a99c |
| identifier_str_mv | 10.1038/s41598-022-25512-8 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717471 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSOMajedeh Gheytanzadeh (17541927)Alireza Baghban (5159648)Sajjad Habibzadeh (5548580)Karam Jabbour (17541942)Amin Esmaeili (17541204)Amin Hamed Mashhadzadeh (17541945)Ahmad Mohaddespour (17541948)Chemical sciencesPhysical chemistryTheoretical and computational chemistryEngineeringMaterials engineeringCO2reduction electrocatalystsANFISGA and PSO<p dir="ltr">Recently, electrochemical reduction of CO<sub>2</sub> into value-added fuels has been noticed as a promising process to decrease CO<sub>2</sub> emissions. The development of such technology is strongly depended upon tuning the surface properties of the applied electrocatalysts. Considering the high cost and time-consuming experimental investigations, computational methods, particularly machine learning algorithms, can be the appropriate approach for efficiently screening the metal alloys as the electrocatalysts. In doing so, to represent the surface properties of the electrocatalysts numerically, d-band theory-based electronic features and intrinsic properties obtained from density functional theory (DFT) calculations were used as descriptors. Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. The developed ANFIS–PSO and ANFIS–GA showed excellent performance with RMSE of 0.0411 and 0.0383, respectively, the minimum errors reported so far in this field. Additionally, the sensitivity analysis showed that the center and the filling of the d-band are the most determining parameters for the electrocatalyst surface reactivity. The present study conveniently indicates the potential and value of machine learning in directing the experimental efforts in alloy system electrocatalysts for CO<sub>2</sub> reduction.</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/s41598-022-25512-8" target="_blank">https://dx.doi.org/10.1038/s41598-022-25512-8</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>2022-12-02T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-022-25512-8https://figshare.com/articles/journal_contribution/Intelligent_route_to_design_efficient_CO_sub_2_sub_reduction_electrocatalysts_using_ANFIS_optimized_by_GA_and_PSO/24717471CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247174712022-12-02T03:00:00Z |
| spellingShingle | Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO Majedeh Gheytanzadeh (17541927) Chemical sciences Physical chemistry Theoretical and computational chemistry Engineering Materials engineering CO2 reduction electrocatalysts ANFIS GA and PSO |
| status_str | publishedVersion |
| title | Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO |
| title_full | Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO |
| title_fullStr | Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO |
| title_full_unstemmed | Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO |
| title_short | Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO |
| title_sort | Intelligent route to design efficient CO<sub>2</sub> reduction electrocatalysts using ANFIS optimized by GA and PSO |
| topic | Chemical sciences Physical chemistry Theoretical and computational chemistry Engineering Materials engineering CO2 reduction electrocatalysts ANFIS GA and PSO |