Catalytic Activity Prediction of α-Diimino Nickel Precatalysts toward Ethylene Polymerization by Machine Learning

Abstract

The present study explored machine learning methods to predict the catalytic activities of a dataset of 165 α-diimino nickel complexes in ethylene polymerization. Using 25 descriptors as the inputs, the XGBoost model presented the optimal performance among six different algorithms (R2 = 0.999, Rt2 = 0.921, Q2 = 0.561). The results of the analysis indicate that high activity is related to the presence of polarizable atoms and less bulky substituents within the N-aryl group. This approach offers valuable insights on the variation principle of catalytic activity as a function of complex structure, helping to effectively design and optimize α-diimino Ni catalysts with desirable performance.

Keywords

• nickel complex
• catalytic activity
• machine learning
• XGBoost
• SHAP values