Machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction
The depletion of fossil fuels and environmental issues have highlighted the need for a green and sustainable alternative energy source for the future economy. Among the various solutions, hydrogen production from the electrolysis of water is a promising solution. Extensive efforts have been devoted...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156307 |
| _version_ | 1826129325687046144 |
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| author | Zhu, Zhenwu |
| author2 | Kedar Hippalgaonkar |
| author_facet | Kedar Hippalgaonkar Zhu, Zhenwu |
| author_sort | Zhu, Zhenwu |
| collection | NTU |
| description | The depletion of fossil fuels and environmental issues have highlighted the need for a green and sustainable alternative energy source for the future economy. Among the various solutions, hydrogen production from the electrolysis of water is a promising solution. Extensive efforts have been devoted in research to find high-performance electrocatalysts for oxygen evolution reaction (OER) to improve the efficiency of electrolysis of water. In recent years, spinel oxides have gained extensive interest in the field of OER electrocatalyst as they demonstrate excellent catalytic activity while being cost-effective. However, there are numerous spinel oxides and their catalytic performance vary in terms of different compositions. It will be extremely time consuming to measure the catalytic performance for each spinel oxide through experiment in order to determine the optimum catalyst. In this study, machine learning (ML) techniques are adopted to accelerate the discovery of the optimum OER catalyst using basic electronic parameters such as octahedral factor, electronegativity and ionic radii as predictors. Spinel [Li0.25Mn0.75]Mn2O4 oxide is predicted to be a highly active OER catalyst. |
| first_indexed | 2024-10-01T07:38:45Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156307 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:38:45Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1563072022-04-12T06:24:16Z Machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction Zhu, Zhenwu Kedar Hippalgaonkar Lydia Helena Wong School of Materials Science and Engineering LydiaWong@ntu.edu.sg, kedar@ntu.edu.sg Engineering::Computer science and engineering Engineering::Materials The depletion of fossil fuels and environmental issues have highlighted the need for a green and sustainable alternative energy source for the future economy. Among the various solutions, hydrogen production from the electrolysis of water is a promising solution. Extensive efforts have been devoted in research to find high-performance electrocatalysts for oxygen evolution reaction (OER) to improve the efficiency of electrolysis of water. In recent years, spinel oxides have gained extensive interest in the field of OER electrocatalyst as they demonstrate excellent catalytic activity while being cost-effective. However, there are numerous spinel oxides and their catalytic performance vary in terms of different compositions. It will be extremely time consuming to measure the catalytic performance for each spinel oxide through experiment in order to determine the optimum catalyst. In this study, machine learning (ML) techniques are adopted to accelerate the discovery of the optimum OER catalyst using basic electronic parameters such as octahedral factor, electronegativity and ionic radii as predictors. Spinel [Li0.25Mn0.75]Mn2O4 oxide is predicted to be a highly active OER catalyst. Bachelor of Engineering (Materials Engineering) 2022-04-12T06:24:16Z 2022-04-12T06:24:16Z 2022 Final Year Project (FYP) Zhu, Z. (2022). Machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156307 https://hdl.handle.net/10356/156307 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Engineering::Materials Zhu, Zhenwu Machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction |
| title | Machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction |
| title_full | Machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction |
| title_fullStr | Machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction |
| title_full_unstemmed | Machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction |
| title_short | Machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction |
| title_sort | machine learning discovery of multi metal oxide catalyst for oxygen evolution reaction |
| topic | Engineering::Computer science and engineering Engineering::Materials |
| url | https://hdl.handle.net/10356/156307 |
| work_keys_str_mv | AT zhuzhenwu machinelearningdiscoveryofmultimetaloxidecatalystforoxygenevolutionreaction |