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

preprint
submitted on 27.06.2020 and posted on 27.07.2020 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

Funding

Chevron Phillips

History

Email Address of Submitting Author

dhe@chem.byu.edu

Institution

Brigham Young University

Country

United States

ORCID For Submitting Author

0000-0001-5689-9762

Declaration of Conflict of Interest

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

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