In this issue of Chem Catalysis, Mao et al. develop machine learning models that predict terminal alkene regioselectivity in catalytic hydroformylation, showing that high temperature, low pressure, and low metal concentration favor linear products. These models enable high-throughput screening, potentially advancing innovations in this industrial process.

中文翻译：

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通过机器学习根据文献数据预测加氢甲酰化区域选择性

在本期《化学催化》中，Mao 等人。开发机器学习模型来预测催化加氢甲酰化中末端烯烃的区域选择性，表明高温、低压和低金属浓度有利于线性产物。这些模型可实现高通量筛选，有可能推动该工业过程的创新。