Decoding polyethylene formation in Cr/PNP catalyzed ethylene oligomerization via experimentally guided machine...

Area of Science:

• Catalysis
• Polymer Chemistry
• Computational Chemistry

Background:

• Polyethylene formation is a significant side reaction in ethylene oligomerization, reducing desired alpha-olefin yields.
• Current strategies for predicting and controlling polyethylene formation are insufficient for efficient catalyst design.

Purpose of the Study:

• To develop a predictive framework for identifying catalysts that minimize polyethylene formation.
• To establish a broadly applicable method for catalyst design and side-product control in homogeneous catalysis.

Main Methods:

• An automated workflow was created for building a chromium/bis(phosphino)amine (Cr/PNP) catalyst library.
• Molecular descriptors were extracted and integrated with machine learning models trained on experimental polyethylene (PE) data.
• A combined classification-regression approach was employed for PE prediction.

Main Results:

• The machine learning models accurately identified low-PE catalysts and predicted PE content.
• Ligand intrinsic properties were identified as the primary determinant of PE production (low vs. high).
• Optimizing experimental conditions further reduced PE in catalysts with inherently low PE production.

Conclusions:

• A broadly applicable framework for catalyst design and side-product control in homogeneous catalysis was established.
• The study highlights the importance of ligand structure in controlling polyethylene formation.
• This approach offers a pathway to enhance alpha-olefin yields by minimizing polyethylene side reactions.