Insight into structural distribution of heterogeneous Ziegler–Natta catalyst from non-empirical structure determination

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.

Preventing Premature Convergence in Evolutionary Structure Determination of Complex Molecular Systems: Demonstration in Few-Nanometer-Sized TiCl4-Capped MgCl2 Nanoplates

The combination of genetic algorithm-based global search and local geometry optimization enables nonempirical structure determination for complex materials such as practical solid catalysts. However, premature convergence in the genetic algorithm hinders the determination of the global minimum for complicated molecular systems. Here, we implemented a distributed genetic algorithm based on the migration from a structure database for avoiding the premature convergence, and thus we realized the structure determination for TiCl₄-capped MgCl₂ nanoplates with experimentally consistent sizes. The obtained molecular models are featured with a realistic size and nonideal surfaces, representing actual primary particles of catalysts.

Effect of Internal Donors on Raman and IR Spectroscopic Fingerprints of MgCl2/TiCl4 Nanoclusters Determined by Machine Learning and DFT

To go deep into the origin of MgCl2 supported Ziegler-Natta catalysis we need to fully understand the structure and properties of precatalytic nanoclusters MgCl2/TiCl4 in presence of Lewis bases as internal donors (ID). In this work MgCl2/TiCl4 nanoplatelets derived by machine learning and DFT calculations have been used to model the interaction with ethyl-benzoate EB as ID, with available exposed sites of binary TixCly/MgCl2 systems. The influence of vicinal Ti2Cl8 and coadsorbed TiCl4 on energetic, structural and spectroscopic behaviour of EB has been considered. The adsorption of homogeneous-like TiCl4EB and TiCl4(EB)2 at the various surface sites have been also simulated. B3LYP-D2 and M06 functionals combined with TZVP quality basis set have been adopted for calculations. The adducts have been characterized by computing IR and Raman spectra that have been found to provide specific fingerprints useful to identify surface species; IR spectra have been successfully compared to available experimental data.