KIEA-IRES Exploration II: Morris Sensitivity-Driven Chemical Network Refinement

Moritz Bensberg and Markus Reiher from ETH Zurich produced this dataset for automated reaction network exploration of Eschemoser-Claisen rearrangements. It applies Morris sensitivity analysis to guide targeted refinement of networks derived from first-principles calculations. The data supports the 2023 arXiv paper 'Uncertainty-aware First-principles Exploration of Chemical Reaction Networks'.

Use Cases

• Benchmarking sensitivity analysis methods for chemical network refinement based on reaction energy data.
• Training or validating automated reaction pathfinding algorithms using allyl and furfuryl alcohol rearrangement data.
• Studying the impact of parameter uncertainty on predicted reaction pathways in first-principles simulations.
• Developing uncertainty quantification frameworks for computational organic chemistry workflows.

Strengths

• Data originates from a peer-reviewed research institution (ETH Zurich) and accompanies a published arXiv paper.
• Focuses on a specific, well-defined chemical system (Eschemoser-Claisen rearrangements) enabling detailed mechanistic study.
• Employs a targeted refinement methodology (Morris sensitivity analysis) to improve computational efficiency.

Limitations

• Column names and dataset structure are not described, limiting immediate usability.
• Exact size (row count) and specific file formats are unknown across all sources.
• Metadata such as the last update date is missing, making freshness difficult to assess.

Provenance

Source

Moritz Bensberg, Markus Reiher, ETH Zurich

Collection Method

Automated reaction network exploration using first-principles calculations and Morris sensitivity analysis.