scholarly journals Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

2020 ◽  
Vol 11 (35) ◽  
pp. 9665-9674
Author(s):  
Steven M. Maley ◽  
Doo-Hyun Kwon ◽  
Nick Rollins ◽  
Johnathan C. Stanley ◽  
Orson L. Sydora ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transition State ◽  
Data Science ◽  
Catalyst Design ◽  
Quantum Mechanical ◽  
Important Goal ◽  
Design Features ◽  
Use Of Data ◽  
Mechanical Transition ◽  
Olefin Oligomerization

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science.

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

2020 ◽  
Author(s):  
Steven Maley ◽  
Doo-Hyun Kwon ◽  
Nick Rollins ◽  
Johnathan Stanley ◽  
Orson Sydora ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Transition State ◽  
Data Science ◽  
Catalyst Design ◽  
Ethylene Oligomerization ◽  
State Model ◽  
General Strategy ◽  
Design Features ◽  
Molecular Catalyst

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<br>

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

2020 ◽  
Author(s):  
Steven Maley ◽  
Doo-Hyun Kwon ◽  
Nick Rollins ◽  
Johnathan Stanley ◽  
Orson Sydora ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Transition State ◽  
Data Science ◽  
Catalyst Design ◽  
Ethylene Oligomerization ◽  
State Model ◽  
General Strategy ◽  
Design Features ◽  
Molecular Catalyst

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<br>

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