Structure-Based Relative Energy Prediction Model: A Case Study of Pd(II)-Catalyzed Ethylene Polymerization and the Electronic Effect of Ancillary Ligands

Abstract

Rapidly mapping a reaction energy profile to understand the reaction mechanism is of great importance and highly desired for the discovery of new chemical reactions. Herein, a combination of density functional theory (DFT) calculations and regression analysis has been applied to construct quantitative structures-based energy prediction models, considering Pd(II)-catalyzed ethylene polymerization as an example, for rapid construction of the reaction energy profile. It is inspiring that only geometrical parameters of the reaction center of one species are capable of predicting the whole energy profile with high accuracy. The reaction energies of ethylene insertion and β-H elimination, which directly correlate with polymerization activity and the possibility of branch formation, were studied to elucidate the electronic effects of ancillary ligands. Further analyses of these models from the statistical and chemical points of view afforded useful information on the design of the catalyst ligand. The current work is expected to methodologically shed new light on rapidly mapping the energy profile of chemical reactions and further provide useful information for the development of the reactions.

Using a single complex to predict the reaction energy profile: a case study of Pd/Ni-catalyzed ethylene polymerization

“…The key is to determine which types of information should be extracted. We previously reported the geometrical structure-based features, including several significant geometrical parameters of the non-ligand fragment, 22 to predict the relative energy data of palladium-catalyzed ethylene insertion and β-H elimination. In this work, we aimed to develop more comprehensive descriptors to enrich the feature set and explore the possibility of predicting more elementary reactions during ethylene polymerization.…”

Quantitative structure–property relationship study of constrained geometry catalysts for olefin polymerization

“…In recent years, computational chemistry has been widely applied to investigate the mechanisms of transition-metal catalyzed reactions, and the relationship between catalyst structures and catalytic performance has been explored at the molecular level. [14][15][16] In this context, a quantitative structureproperty relationship (QSPR) model has been employed to quantitatively study the impact of molecular descriptors of catalysts on catalytic performance, [17][18][19] as well as to predict novel catalyst molecules. [20][21][22] There have been theoretical calculations on the CGC polymerization mechanism as well.…”

Mechanistic Inference from Statistical Models at Different Data-Size Regimes

“…Recent technological advancements allow for fast data production − and provide better tools for mining, organizing, visualizing, and interrogating data. The statistical toolbox has been used to predict quantitative reaction outcomes (e.g., yield and selectivity) − bond dissociation energies (BDE), , and radical stability, to identify reactive sites, − and to suggest retrosynthetic routes. − These and many more applications, which demonstrate the power of mathematical models as a predictive tool in synthetic chemistry, have been described elsewhere and are not the focus of this Perspective. − Herein, we wish to discuss the use and limitations of such tools in mechanistic investigation with a particular focus on chemically meaningful molecular descriptors in homogeneous catalysis. ,− …”