Same but Different: How Permutation Degeneracy Enhances Machine Learning in Single-Atom Catalyst Discovery

Key Points

• The research aims to enhance the prediction of catalytic performance in single-atom catalysts by addressing permutation degeneracy.
• Utilized machine learning models to predict catalytic performance.
• Incorporated permutation degeneracy into the model framework.
• Analyzed various metal coordination environments to assess structural diversity.
• Improved prediction accuracy for catalytic performance of SACs.
• Highlighting the significance of structural diversity in catalysts.
• Demonstrated the effectiveness of permutation degeneracy in enhancing model performance.