Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst
Electrochemical CO2 reduction reaction (CO2RR) is an important process which is a potential way to recycle excessive CO2 in the atmosphere. Although the electrocatalyst is the key toward efficient CO2RR, the progress of discovering effective catalysts is lagging with current methods. Because of the...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/168939 |
| _version_ | 1811677158057181184 |
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| author | Hu, Erhai Liu, Chuntai Zhang, Wei Yan, Qingyu |
| author2 | School of Materials Science and Engineering |
| author_facet | School of Materials Science and Engineering Hu, Erhai Liu, Chuntai Zhang, Wei Yan, Qingyu |
| author_sort | Hu, Erhai |
| collection | NTU |
| description | Electrochemical CO2 reduction reaction (CO2RR) is an important process which is a potential way to recycle excessive CO2 in the atmosphere. Although the electrocatalyst is the key toward efficient CO2RR, the progress of discovering effective catalysts is lagging with current methods. Because of the cost and time efficiency of the modern machine learning (ML) algorithm, an increasing number of researchers have applied ML to accelerate the screening of suitable catalysts and to deepen our understanding in the mechanism. Hence, we reviewed recent applications of ML in the research of CO2RR by the types of electrocatalyst. An introduction on the general methodology and a discussion on the pros and cons for such applications are included. |
| first_indexed | 2024-10-01T02:32:55Z |
| format | Journal Article |
| id | ntu-10356/168939 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:32:55Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1689392024-06-11T15:37:54Z Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst Hu, Erhai Liu, Chuntai Zhang, Wei Yan, Qingyu School of Materials Science and Engineering Institute of Materials Research and Engineering, A*STAR Engineering Carbon dioxide Machine learning Electrochemical CO2 reduction reaction (CO2RR) is an important process which is a potential way to recycle excessive CO2 in the atmosphere. Although the electrocatalyst is the key toward efficient CO2RR, the progress of discovering effective catalysts is lagging with current methods. Because of the cost and time efficiency of the modern machine learning (ML) algorithm, an increasing number of researchers have applied ML to accelerate the screening of suitable catalysts and to deepen our understanding in the mechanism. Hence, we reviewed recent applications of ML in the research of CO2RR by the types of electrocatalyst. An introduction on the general methodology and a discussion on the pros and cons for such applications are included. Ministry of Education (MOE) Submitted/Accepted version Q.Y. acknowledges the funding support from Singapore MOE AcRF Tier 1 under Grant No. 2020-T1-001-031. 2023-06-23T03:19:44Z 2023-06-23T03:19:44Z 2023 Journal Article Hu, E., Liu, C., Zhang, W. & Yan, Q. (2023). Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst. Journal of Physical Chemistry C, 127(2), 882-893. https://dx.doi.org/10.1021/acs.jpcc.2c08343 1932-7447 https://hdl.handle.net/10356/168939 10.1021/acs.jpcc.2c08343 2-s2.0-85146164646 2 127 882 893 en MOE-2020-T1-001-031 Journal of Physical Chemistry C © 2023 American Chemical Society. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1021/acs.jpcc.2c08343. application/pdf |
| spellingShingle | Engineering Carbon dioxide Machine learning Hu, Erhai Liu, Chuntai Zhang, Wei Yan, Qingyu Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst |
| title | Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst |
| title_full | Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst |
| title_fullStr | Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst |
| title_full_unstemmed | Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst |
| title_short | Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst |
| title_sort | machine learning assisted understanding and discovery of co₂ reduction reaction electrocatalyst |
| topic | Engineering Carbon dioxide Machine learning |
| url | https://hdl.handle.net/10356/168939 |
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