DFT Screening of Transition Metal Phthalocyanine Sheets for Electrocatalysis

Muhammad Rafiq's dataset contains results from a systematic density functional theory (DFT) investigation of 3d-transition metal phthalocyanine sheets. The 16.4 KB dataset, published in 2026, screens a series of metals from Sc to Zn for their catalytic activity in hydrogen evolution, oxygen evolution, and oxygen reduction reactions. It includes stability analyses and supervised machine learning models for predicting catalyst efficiency.

Use Cases

• Predict hydrogen adsorption energy (ΔG_H*) for HER activity using atomic and electronic descriptors via KNN models.
• Identify electrochemically stable catalysts like s-VPc and s-CoPc by analyzing formation energy and dissolution potential data.
• Screen for low overpotential candidates for OER by evaluating calculated values, such as the 0.41 V for s-VPc.
• Classify bifunctional catalyst efficiency for HER/OER or OER/ORR using machine learning on DFT-derived features.
• Compare ORR performance of s-CoPc and s-MnPc against benchmark Pt(111) surface data.

Strengths

• Systematic screening of 10 transition metals (Sc to Zn) for three key electrochemical reactions.
• Includes multi-faceted stability analysis covering formation energy, dissolution potential, and surface Pourbaix diagrams across all pH.
• Integrates supervised machine learning with KNN models for predictive catalyst classification.

Limitations

• The dataset is very small at 16.4 KB, indicating limited scope and likely a summary of results rather than raw computational outputs.
• All data is derived from computational simulations (DFT), not experimental validation, which may limit real-world applicability.
• Specific row counts, column names, and the full feature set for ML are not provided in the description.

Provenance

Source

Muhammad Rafiq via figshare.

Collection Method

Data generated from first-principles density functional theory (DFT) calculations and subsequent machine learning analysis.

Freshness

Last updated in April 2026, indicating recent computational work.