A dataset for CO2 cycloaddition with ethylene oxide over metal oxide catalysts
Metal oxide catalysts have emerged as promising materials for CO2 cycloaddition reactions due to their tunable composition, facile separation, reusability, and low cost. However, systematic investigations remain limited, and a comprehensive understanding of reaction mechanisms is hindered by the lack of extensive, well-curated datasets. This study establishes a systematic dataset of 102 metal oxide catalysts, including layered double hydroxide (LDH) and ZnO, with variations in metal dopant type and ratio, defect characteristics, and crystal plane orientation. Using high-throughput first-principles calculations, we generated a multi-dimensional dataset containing elementary reaction energies, vibrational frequencies, Bader charges, and density of states. A rigorous two-tiered quality control protocol ensures data integrity. The dataset reveals structure-performance relationships linking catalyst structural features to electronic descriptors (e.g., Bader charge transfer, p-band centers of O atoms, d-band centers of metal atoms) and catalytic activity. This work provides a reliable foundation for exploring catalytic performance and reaction mechanisms, and demonstrates how high-throughput calculations can generate domain-specific, mechanistically explicit data. Future efforts will focus on developing feature extraction code for seamless integration with machine learning frameworks, and the dataset will be continuously enriched through experimental validation and remain openly accessible.