Experimentally-based Fe-catalyzed ethene oligomerization machine learning model provides highly accurate prediction of propagation/termination selectivity
Bo Yang, Anthony J. Schaefer, Brooke L. Small, Julie A. Hopkins Leseberg, Steven M. Bischof, Michael S. Webster‐Gardiner, Daniel H. Ess
Abstract
-value for a new Fe phosphaneyl-pyridinyl-quinoline catalyst followed by experimental measurement that showed precise agreement. In addition to quantitative predictions, we demonstrate how this machine learning model can provide qualitative catalyst design using proximity of pairs type analysis.
Topics & Concepts
BespokeSelectivityCatalysisWork (physics)Biological systemComputer scienceChemistryMaterials scienceArtificial intelligenceOrganic chemistryThermodynamicsPhysicsBiologyPolitical scienceLawMachine Learning in Materials ScienceOrganometallic Complex Synthesis and CatalysisCO2 Reduction Techniques and Catalysts