Copper Carbene Catalytic N2 Fixation: DFT Insights and Predictive Model Data

Density functional theory calculations underpin a mechanism-driven design for catalytic nitrogen fixation using copper carbenes. The dataset, authored by Weichi Chen and updated in April 2026, likely contains electronic descriptors and activation barriers derived from quantum chemical analysis. It enables the quantitative prediction of catalytic performance through a multivariate regression model.

Use Cases

• Training predictive models for catalytic activity based on quantum descriptors like Hirshfeld charges.
• Benchmarking catalyst designs for nitrogen fixation based on calculated activation barriers.
• Analyzing orbital interaction mechanisms in small-molecule activation based on Principal Interacting Orbital (PIO) contributions.
• Validating computational workflows for reaction cycle design based on DFT-derived insights.

Strengths

• Data is derived from detailed density functional theory (DFT) calculations, providing a quantum mechanical foundation.
• Includes a multivariate regression model incorporating electronic descriptors for quantitative prediction.
• File size is 1.9 MB, indicating a focused and manageable dataset for analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect the theoretical bias inherent to computational chemistry studies on figshare.

Provenance

Source

figshare, author Weichi Chen

Collection Method

Generated via density functional theory (DFT) calculations and subsequent analysis.

Time Range

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Freshness

Last updated 2026-04-21 04:14:01; freshness should be verified.

Geography

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