
One experiment, two insights. Conventional kinetic analysis uses time-dependent yield data to determine rate constants, requiring experiments separate from those used for reaction optimization. CYAN instead uses concentration-dependent yield data from optimization experiments, augmented by machine learning. Rate equations developed by chemists are then applied to extract rate constants, allowing a single set of experiments to provide insights into both reaction optimization and kinetics.
©2026 Isobe et al.
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Scientific Frontline: Extended "At a Glance" Summary: Concentration-Dependent Yield Analysis (CYAN)
The Core Concept: Concentration-dependent yield analysis (CYAN) is a novel method combining machine learning and chemical rate equations to extract hidden kinetic information—specifically reaction speeds—from yield data obtained during standard reaction optimization experiments.
Key Distinction/Mechanism: Unlike traditional methods that require separate kinetic experiments mapping yield against time to understand reaction mechanisms, CYAN utilizes concentration-dependent yield data from existing optimization experiments, augmented by machine learning, to calculate rate constants.
Major Frameworks/Components:
- Machine Learning Augmentation: Fills gaps between experimental results to create a complete picture of product concentration changes under varying conditions.
- Chemical Rate Equations: Applied by chemists based on mechanistic hypotheses to extract rate constants from the augmented data.
- Nickel-Mediated Reaction Testing: Demonstrated CYAN's efficacy by analyzing a reaction building large ring-shaped carbon molecules, revealing an unexpected "template effect" where nickel retarded a secondary competing pathway to increase target molecule yield.


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