ArticleSmart molecules : open access2026
BayesianKAN: A reaction condition optimization framework integrating Kolmogorov-Arnold network and Bayesian optimization.
Article in Smart molecules : open access, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Efficient optimization of chemical reaction conditions is crucial for enhancing reaction yield and selectivity, yet traditional methods face inherent limitations including experimental inefficiency, low predictive accuracy, and poor interpretability. This study proposes a novel framework for reaction condition optimization by integrating the Kolmogorov-Arnold network (KAN) model and Bayesian optimization (BO) algorithm. The KAN model establishes accurate and explicit mappings between reaction conditions and outcomes like yield, while BO iteratively optimizes reaction outcomes to identify optimal conditions based on the KAN model, demonstrating efficacy even with sparse data. This framework is implemented as the BayesianKAN software and validated in two reaction systems: hydrogen peroxide (H
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