RxnOptBench: Benchmarking LLMs for Reaction-Condition Optimization in Organic Methodology
Chemical reaction-condition optimization -- choosing the catalyst, ligand, solvent, reagent, temperature, time, and atmosphere that jointly maximize yield and stereoselectivity -- is a central, judgement-laden subtask of organic methodology research that large language models are increasingly expected to support. Yet existing chemistry benchmarks evaluate reaction-class labelling, retrosynthesis, or SMILES manipulation, and do not ask models to read a real condition-screening table and pick the best set. We introduce RxnOptBench, a benchmark whose every option and precedent is a real wet-lab entry mined from the optimization tables of organic-methodology papers published in 2025, graded b...
| Authors | Lingli Ge, Yubin Wang, Junyuan Gao et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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