Asymmetric catalysis plays a crucial role in advancing medicine and materials science. However, the prevailing experiment-driven methods for catalyst evaluation are both resource-heavy and time-consuming. To address this challenge, we present CatScore - a learning-centric metric designed for the automatic evaluation of catalyst design models at both instance and system levels. This approach harnesses the power of deep learning to predict product selectivity as a function of reactants and the proposed catalyst. The predicted selectivity serves as a quantitative score, enabling a swift and precise assessment of a catalyst’s activity. On an instance level, CatScore’s predictions correlate closely with experimental outcomes, demonstrating a Spearman’s ρ = 0.84, which surpasses the density functional theory (DFT) with ρ = 0.54 and round-trip accuracy metrics at ρ = 0.24. Importantly, when ranking catalyst candidates, CatScore achieves a mean reciprocal ranking significantly superior to traditional DFT methods, marking a considerable reduction in labor and time investments needed to find top-performing catalysts.
CatScore: A High-Efficiency Evaluation Metric for Asymmetric Catalyst Design
11 September 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.