Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols
Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols
复制标题
用于预测镍(II)盐诱导苯酚合成中有机光敏剂催化活性的机器学习分类
DOI：
10.1002/anie.202219107
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Saito Susumu
中科院分区：
文献类型：
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作者：
Noto Naoki;Yada Akira;Yanai Takeshi;Saito Susumu
使用少量有机光敏剂活化无机（按需无配体）镍（II）盐的催化体系代表了交叉偶联反应的成本效益方法，而C（sp2）−O键的形成仍然不太发达。在此，我们报告了一种策略，用于合成苯酚与镍（II）盐和有机光敏剂，这是通过调查60有机光敏剂的催化活性，包括各种电子供体和受体部分确定。为了研究多个难处理参数对光敏剂催化活性的影响，开发了机器学习（ML）模型，其中我们嵌入了代表其物理和结构特性的描述符，这些描述符分别从DFT计算和RDKit获得。该研究澄清了在ML模型中集成DFT和RDKit衍生的描述符，相对于仅使用两个描述符集之一，在广泛的搜索空间中平衡了更高的“精确度”和“召回率”。
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(sp2)−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.