Accelerated discovery of M6@g-N4 catalysts for CO2 electroreduction via machine learning and DFT: Descriptor engineering and activity trend validation

Key Points

• This research aims to accelerate the discovery of efficient M6@g-N4 catalysts for CO2 electroreduction.
• Utilized machine learning techniques to predict catalyst performance
• Applied density functional theory (DFT) for validating results
• Engineered descriptors to enhance activity trends
• Identified promising candidates among M6@g-N4 catalysts for CO2 reduction
• Showed improved prediction accuracy in electroreduction activities
• Validated descriptor engineering as a key strategy for rapid development