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Quantum-Mechanical Transition-State Model Combined with Machine Learning Provides Catalyst Design Features for Selective Cr Olefin Oligomerization

submitted on 27.06.2020, 18:13 and posted on 27.07.2020, 05:06 by Steven Maley, Doo-Hyun Kwon, Nick Rollins, Johnathan Stanley, Orson Sydora, Steven M. Bischof, Daniel Ess
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


Chevron Phillips


Email Address of Submitting Author


Brigham Young University


United States

ORCID For Submitting Author


Declaration of Conflict of Interest

A patent application has been filed for subject matter contained in this article.