Key Takeaways & Executive Findings
- •• • The dataset comprises 102 metal oxide catalysts, including LDH and ZnO, with systematic variations in dopant type/ratio, defect type, and crystal plane orientation, enabling high-throughput screening for CO2 cycloaddition. • • First-principles calculations provide elementary reaction energies, vibrational frequencies, Bader charges, and density of states, offering atomic-scale mechanistic insight into the cycloaddition pathway. • • Electronic structure analysis identifies Bader charge transfer of EO/CO2 and p/d-band centers as key descriptors linking catalyst structure to activity, facilitating rational catalyst design. • • The dataset is openly accessible with complete raw calculation files, supporting data-driven research and integration with machine learning frameworks for accelerated catalyst discovery.
Abstract
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.
1. Introduction
Industrial synthesis of cyclic carbonates via CO2 cycloaddition with ethylene oxide (EO) currently relies on homogeneous catalysts such as KI/polyethylene glycol and quaternary ammonium salts. These systems suffer from complicated product separation and residual halogen contamination, driving the need for efficient, halogen-free, and solvent-free heterogeneous alternatives. Metal oxides, including layered double hydroxides (LDH) and ZnO, offer tunable composition, facile separation, and low cost, but their catalytic activity remains suboptimal.
Recent strategies to enhance activity include introducing secondary metal dopants, adjusting dopant ratios, engineering surface defects, and controlling crystal plane orientations. However, systematic theoretical understanding of how these factors affect the reaction mechanism is lacking, hindering rational catalyst design. This study addresses this gap by constructing a comprehensive dataset of 102 metal oxide catalysts via high-throughput first-principles calculations, correlating structural features with electronic descriptors and catalytic performance, thereby providing a foundation for data-driven catalyst optimization.
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XI Jianying, WANG Baojun, ZHANG Riguang (2026). A dataset for CO2 cycloaddition with ethylene oxide over metal oxide catalysts. Journal of Fuel Chemistry and Technology. https://doi.org/10.1016/S1872-5813(26)60743-8
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Frequently Asked Questions
What specific metal dopants and ratios are included in the dataset, and how do they affect catalytic activity?
The dataset includes variations in metal dopant type and ratio for LDH and ZnO catalysts. While specific dopants are not enumerated in the abstract, the study systematically varies these parameters to reveal their impact on elementary reaction energies and electronic descriptors, such as Bader charge transfer and d-band centers, which correlate with catalytic activity.
How does the dataset ensure reliability and reproducibility of the computational results?
A rigorous two-tiered quality control protocol is applied to both computational parameter settings and output results, ensuring integrity and reliability. Complete raw calculation files are provided, allowing independent verification and further analysis.
What are the key electronic descriptors that link catalyst structure to performance, and how can they be used for catalyst design?
Key descriptors include Bader charge transfer of EO or CO2, p-band centers of O atoms, and d-band centers of metal atoms. These descriptors correlate with catalytic activity, providing screening criteria for rational design of efficient metal oxide catalysts.
How can this dataset be integrated with machine learning frameworks for predictive modeling?
The dataset is designed to be machine-readable and will be complemented by feature extraction code that is easily integrated and cross-platform. This will enable seamless integration with mainstream machine learning libraries, facilitating the development of predictive models for catalyst performance.
What are the limitations of the current dataset, and how will it be expanded in the future?
The dataset currently focuses on LDH and ZnO catalysts; future expansion will include experimental validation and continuous enrichment. The authors plan to develop portable feature extraction tools to reduce computational resource consumption and enhance data exchangeability, broadening the dataset's applicability.
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