Methylene Cycloalkene Hydroamination Reaction Data

A collection of computational and experimental data supporting the first enantioselective hydroamination of methylene cycloalkenes. It includes results from a machine-learning model based on universal descriptors and data-augmentation, as well as findings from deuterium-labeling experiments and density functional theory calculations. The data is associated with a palladium-catalyzed reaction producing chiral tertiary amines.

Use Cases

• Train a machine-learning model using universal descriptors from the UD-qTS strategy to predict reaction outcomes.
• Analyze the ligand-to-ligand hydrogen transfer (LLHT) pathway identified as rate-determining through DFT calculations.
• Study the regioselective C3-N1 bond formation mechanism described for the hydroamination reaction.
• Validate the physicochemically grounded data-augmentation protocol used to accelerate reaction optimization.

Strengths

• Data is derived from a published study reporting the first enantioselective functionalization of methylene cycloalkenes.
• Includes results from a machine-learning model based on the UD-qTS strategy and a data-augmentation protocol.
• Findings are supported by deuterium-labeling experiments and density functional theory calculations.

Limitations

• The dataset is very small, with a file size of only 22.7 KB, indicating limited scope and sample size.
• Specific data structure, row count, and column definitions are not provided in the input.
• The dataset is focused on a specific chemical reaction, limiting its generalizability to other systems.

Provenance

Source

figshare

Collection Method

Generated from computational modeling (DFT, machine learning) and experimental chemistry (deuterium-labeling, synthesis).

Freshness

Last updated on 2026-03-20.