Computational and Experimental Data for Homogeneous Transfer Hydroformylation Catalysts

Jyothish Joy's research dataset contains computational and experimental data for designing homogeneous transfer hydroformylation catalysts. The work involved density functional theory calculations, data science analysis, and experimental validation to screen over 600 bisphosphine ligands from a ReaLigands library. The dataset was last updated in March 2026.

Use Cases

• Apply data science methods to screen the >600 bisphosphine ligands from the ReaLigands library for catalytic activity.
• Use density functional theory calculation results to develop interpretable chemical space mapping for bisphosphine ligands.
• Validate experimentally synthesized bisphosphine ligands identified as promising from computational screening.
• Analyze experimental screening data for commercially available and common bisphosphines to establish baseline reactivity.

Strengths

• Dataset is based on a multi-method approach combining density functional theory calculations, data science, and experimental validation.
• Includes screening data for over 600 bisphosphine ligands from a specialized ReaLigands library.
• Focuses on a specific catalytic challenge where only a single known bisphosphine ligand combined with Rh provides high reactivity.

Limitations

• Dataset size is small at 132.7 KB, indicating limited scope and likely a small number of records.
• The specific data structure, column names, and row count are unknown, complicating direct analysis.
• Data is focused on a very specific chemical system (homogeneous transfer hydroformylation), limiting generalizability.

Provenance

Source

Jyothish Joy via figshare.

Collection Method

Generated through density functional theory calculations, data science analysis, and experimental screening/synthesis.

Freshness

Last updated in March 2026.