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Machine Learning of Photocatalytic Reductive Coupling of Aldehydes and Its Interpretation Using SHapley Additive
Jinya Li1, Tongyu Han1, Quansheng Mou1
1State Key Laboratory of Applied Organic Chemistry, College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou 730000, China.
Abstract:
Photocatalytic coupling reactions are valuable for sustainable synthesis, yet their development and optimization still rely on empirical time-consuming screening. Using 439 literature data points, we developed an interpretable machine learning workflow for photocatalytic aldehyde coupling reactions. SHAP analysis identified key steric and electronic descriptors, while external validation supported the model robustness. These data-driven approaches provide practical guidance for reaction understanding and optimization.
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Metals like palladium, platinum, and nickel are commonly used in their solid forms — fine powder on an inert surface. As these catalysts remain insoluble in the reaction mixture, they are referred to as heterogeneous catalysts.
The hydrogenation process takes place on the surface of...

