From density functional theory to machine learning predictive models for electrical properties of spinel oxides
Abstract This work focuses on predicting and characterizing the electronic conductivity of spinel oxides, which are promising materials for energy storage devices and for the oxygen evolution and oxygen reduction reactions due to their attractive properties and abundance of transition metals that ca...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-62788-4 |
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| _version_ | 1841559519957614592 |
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| author | Yuval Elbaz Maytal Caspary Toroker |
| author_facet | Yuval Elbaz Maytal Caspary Toroker |
| author_sort | Yuval Elbaz |
| collection | DOAJ |
| description | Abstract This work focuses on predicting and characterizing the electronic conductivity of spinel oxides, which are promising materials for energy storage devices and for the oxygen evolution and oxygen reduction reactions due to their attractive properties and abundance of transition metals that can act as active sites for catalysis. To this end, a new database was developed from first principles, including band structure and conductivity properties of spinel oxides, and machine learning algorithms were trained on this database to predict electronic conductivity and band gaps based solely on the compositions. The models developed in this study are scaled from the quantum level up to a continuum conductivity model. The relatively small database used in this study allowed for accurate predictions of band gap and conductivity. By altering the composition of spinel oxides, the model was able to predict high conductivity for spinels with high nickel content and to match experimental trends for manganese cobalt spinels. The ability to predict material properties is especially important in energy conversion devices such as batteries and supercapacitors where redox reactions take place. |
| format | Article |
| id | doaj-art-d019e01890a74de7a711c9d6d678129d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d019e01890a74de7a711c9d6d678129d2025-01-05T12:25:45ZengNature PortfolioScientific Reports2045-23222024-05-0114111410.1038/s41598-024-62788-4From density functional theory to machine learning predictive models for electrical properties of spinel oxidesYuval Elbaz0Maytal Caspary Toroker1Department of Materials Science and Engineering, Technion – Israel Institute of TechnologyDepartment of Materials Science and Engineering, Technion – Israel Institute of TechnologyAbstract This work focuses on predicting and characterizing the electronic conductivity of spinel oxides, which are promising materials for energy storage devices and for the oxygen evolution and oxygen reduction reactions due to their attractive properties and abundance of transition metals that can act as active sites for catalysis. To this end, a new database was developed from first principles, including band structure and conductivity properties of spinel oxides, and machine learning algorithms were trained on this database to predict electronic conductivity and band gaps based solely on the compositions. The models developed in this study are scaled from the quantum level up to a continuum conductivity model. The relatively small database used in this study allowed for accurate predictions of band gap and conductivity. By altering the composition of spinel oxides, the model was able to predict high conductivity for spinels with high nickel content and to match experimental trends for manganese cobalt spinels. The ability to predict material properties is especially important in energy conversion devices such as batteries and supercapacitors where redox reactions take place.https://doi.org/10.1038/s41598-024-62788-4 |
| spellingShingle | Yuval Elbaz Maytal Caspary Toroker From density functional theory to machine learning predictive models for electrical properties of spinel oxides Scientific Reports |
| title | From density functional theory to machine learning predictive models for electrical properties of spinel oxides |
| title_full | From density functional theory to machine learning predictive models for electrical properties of spinel oxides |
| title_fullStr | From density functional theory to machine learning predictive models for electrical properties of spinel oxides |
| title_full_unstemmed | From density functional theory to machine learning predictive models for electrical properties of spinel oxides |
| title_short | From density functional theory to machine learning predictive models for electrical properties of spinel oxides |
| title_sort | from density functional theory to machine learning predictive models for electrical properties of spinel oxides |
| url | https://doi.org/10.1038/s41598-024-62788-4 |
| work_keys_str_mv | AT yuvalelbaz fromdensityfunctionaltheorytomachinelearningpredictivemodelsforelectricalpropertiesofspineloxides AT maytalcasparytoroker fromdensityfunctionaltheorytomachinelearningpredictivemodelsforelectricalpropertiesofspineloxides |