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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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/168939 |
| Summary: | 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. |
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