Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models

Nenhuma Miniatura disponível

Data

2024-01-29

Título da Revista

ISSN da Revista

Título de Volume

Editor

Universidade Federal de Goiás

Resumo

In the present study, the stereoselectivity of Claisen Rearrangements was addressed, focusing on the influence of two distinct electron-withdrawing groups and eight different substituents in three variants of the rearrangement: Hurd, Eschenmoser, and Johnson. Using the Curtin-Hammett principle, the energies of reactions, products, and transition states were calculated using the M062X/def2TZVPP theory level. The results indicate that kinetic effects predominantly govern the reaction equilibrium. A key aspect of our investigation involved applying Shubin’s energy decomposition analysis to the optimized transition states. This approach highlighted the significant influence of the electrostatic component on stereoselectivity, revealing its predominance over the quantum and steric components. Moreover, each transition state was divided into four fragments: the electron-withdrawing groups (Ester and Nitrile), the specific Hurd/Esch/John group (H, NMe2, and OEt), various substituents (alkyl and aryl), and the central fragment. This fragmentation allowed for a comprehensive analysis of the dipole moments of the groups and non-covalent interactions, providing insights into the electrostatic forces driving the rearrangement process. In addition, Supervised Machine Learning algorithms were employed, focusing on the analysis of electronic and geometric datasets related to the transition states. The results obtained not only elucidate the mechanisms underlying the stereoselectivity of Claisen Rearrangements but also provide a subtle understanding of the interaction between different molecular components, establishing new perspectives in advanced applications in organic synthesis.

Descrição

Citação

OLIVEIRA, A. G. C. Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models. 2024. 209 f. Tese (Doutorado em Química) - Faculdade de Química, Universidade Federal de Goiás, Goiânia, 2024.