Predicting the catalytic activities of transition metal (Cr, Fe, Co, Ni) complexes towards ethylene polymerization by machine learning

The study aims to execute machine learning (ML) method for building an intelligent prediction system for catalytic activities of a relatively big dataset of 1056 transition metal complex precatalysts in ethylene polymerization. Among 14 different algorithms, the CatBoost ensemble model provides the best prediction with the correlation coefficient (R2) values of 0.999 for training set and 0.834 for external test set. The interpretation of the obtained model indicates that the catalytic activity is highly correlated with number of atom, conjugated degree in the ligand framework, and charge distributions. Correspondingly, 10 novel complexes are designed and predicted with higher catalytic activities. This work shows the potential application of the ML method as a high-precision tool for designing advanced catalysts for ethylene polymerization. Machine learning (ML) methods are applied to investigate the catalytic activities of 1056 transition metal complexes in ethylene polymerization. The CatBoost ensemble model shows the optimal performance (R2 = 0.999, Rt2 = 0.834, Q2 = 0.656). The interpretation of the obtained model indicates that the catalytic activity is highly correlated with number of atom, conjugated degree in the ligand backbone, and charge distributions. Correspondingly, 10 new Co complexes are designed and predicted with higher catalytic activities.image