DFT Data for Iron Phthalocyanine Catalysts in Nitrate Reduction to Ammonia

Density Functional Theory results from a study on functionalized iron phthalocyanine catalysts for electrochemical nitrate reduction. The 710.8 KB dataset includes databases of molecular structures and Python scripts to reproduce three key figures from the associated publication. Amy Wuttke authored this dataset, which was last updated on April 16, 2026.

Use Cases

• Analyze reaction pathways for nitrate reduction based on the provided DFT energy data.
• Compare the impact of different functional groups on catalyst electronic structure based on HOMO/LUMO level data.
• Reproduce published figures on catalyst performance using the included plotting scripts.
• Benchmark new computational models against the provided reference FePc structures.

Strengths

• Includes complete Python scripts for reproducing three figures from the original publication.
• Contains specific databases for reference and adsorbate-laden FePc structures.
• Dataset is small (710.8 KB), ensuring quick download and local processing.

Limitations

• Row count and specific column-level documentation are absent.
• Data is highly specialized for a single, specific electrocatalytic reaction study.
• Freshness should be verified; last updated 2026-04-16 08:59:25.

Provenance

Source

figshare, authored by Amy Wuttke.

Collection Method

Computational data generated via Density Functional Theory (DFT) calculations.

Freshness

Last updated 2026-04-16 08:59:25.