Copper Carbene Catalytic N2 Fixation: DFT Insights and Predictive Model

Weichi Chen published a dataset on figshare in 2026 containing computational results for catalytic nitrogen fixation. The data likely contains quantum chemical descriptors and activation barriers derived from density functional theory (DFT) calculations. It includes a multivariate regression model for predicting catalyst performance.

Use Cases

• Training predictive models for catalyst activity based on quantum descriptors like Hirshfeld charges.
• Benchmarking computational methods for N2 activation mechanisms using provided DFT insights.
• Screening potential catalyst candidates for nitrogen fixation based on the descriptor-based regression model.

Strengths

• Dataset is based on detailed density functional theory (DFT) calculations, providing a mechanistic foundation.
• Includes a quantitative predictive model incorporating electronic descriptors for activation barriers.
• File size is 66.6 KB, indicating a focused and potentially well-structured dataset.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale modeling.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Weichi Chen

Collection Method

Data generated from density functional theory (DFT) calculations and subsequent analysis.

Freshness

Last updated 2026-04-21 04:13:59; freshness should be verified.