The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Solvent selection—a critical yet challenging task in chemistry—is hindered by theoretical modeling difficulties and severe data scarcity, especially for continuous-flow processes. Method: We introduce the first temporal solvent selection benchmark dataset tailored for flow chemistry, encompassing 1,200+ continuous process conditions with high-resolution transient flow-control parameters and corresponding yield labels. To address sparse, sequential process spaces, we propose a temporal regression framework integrating domain-informed feature engineering, transfer learning, and active learning. Contribution/Results: Our method significantly improves prediction accuracy for solvent substitution under low-data regimes, reducing mean absolute error by 32% on average. The dataset fills a key gap in AI for Chemistry—namely, benchmarks for time-series-driven, few-shot solvent replacement—and empirically validates multiple AI strategies for sustainable chemical manufacturing. This work advances reproducible, scalable AI benchmarking in chemistry.