The use of data science tools to provide the emergence of nontrivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene:1- octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr–N distance, Cr–α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene


中文翻译：

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结合机械学习的量子力学过渡态模型为选择性Cr烯烃低聚提供催化剂设计功能

数据科学工具的使用为催化剂设计提供非平凡的化学特征的出现是催化科学的重要目标。另外，目前还没有用于计算均相分子催化剂设计的通用策略。在这里，我们报告了一项实验验证的DFT过渡态模型与随机森林机器学习模型的独特组合，旨在设计用于选择性乙烯低聚的新型分子Cr膦亚胺（Cr（P，N））催化剂，特别是增加1-辛烯选择性。这涉及对105个（P，N）配体的1-己烯：1-辛烯过渡态选择性的计算以及14个描述符的收获，然后将其用于建立随机森林回归模型。该模型显示了几个关键设计特征的出现，例如Cr–N距离，