AI-HTE Reaction Discovery Data for Nitrene-Mediated C–O Coupling

A dataset from figshare describes a machine learning-integrated workflow for discovering carbamate synthesis reactions. The dataset likely contains experimental results from a high-throughput screening of phenolic and alcohol substrates, with reaction yields predicted by a trained model. The work was authored by Jingyuan Liu and uploaded in May 2026.

Use Cases

• Training yield prediction models based on high-throughput experimental data.
• Benchmarking AI-assisted workflows for reaction prioritization and translation.
• Studying substrate scope and functional-group tolerance in catalytic C–O coupling.
• Analyzing the performance of a Ru–nitrenoid pathway with Curtius-type rearrangement.

Strengths

• The dataset is associated with a validated case study published in a scientific workflow.
• The underlying methodology integrates machine learning for prospective prediction of unseen substrates.
• The dataset is licensed under CC-BY-NC-4.0 for non-commercial reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is 69.3 KB, indicating a very limited scope.

Provenance

Source

figshare

Collection Method

Generated from a preregistered, high-throughput experimental workflow integrating machine learning.

Freshness

Last updated 2026-05-22 19:14:58; freshness should be verified.