Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols. (1st February 2023) - Noto, Naoki; Yada, Akira; Yanai, Takeshi; Saito, Susumu
Abstract: Catalytic systems using a small amount of organic photosensitizer for the activation of an inorganic (on‐demand ligand‐free) nickel(II) salt represent a cost‐effective method for cross‐coupling reactions, while C(sp 2 )−O bond formation remains less developed. Herein, we report a strategy for the synthesis of phenols with a nickel(II) salt and an organic photosensitizer, which was identified via an investigation into the catalytic activity of 60 organic photosensitizers consisting of various electron donor and acceptor moieties. To examine the effect of multiple intractable parameters on the catalytic activity of photosensitizers, machine‐learning (ML) models were developed, wherein we embedded descriptors representing their physical and structural properties, which were obtained from DFT calculations and RDKit, respectively. The study clarified that integrating both DFT‐ and RDKit‐derived descriptors in ML models balances higher "precision" and "recall" across a wide range of search space relative to using only one of the two descriptor sets. Abstract : A strategy for the synthesis of phenols with an inorganic nickel(II) salt and an organic photosensitizer has been developed, together with machine‐learning models to predict the catalytic activity of organic photosensitizers. An investigation into the effect of the descriptors clarified that inputting both physical and structural properties of the organic photosensitizers contributes to the construction of robust machine‐learning models.