Work

Hammett neural networks: prediction of frontier orbital energies of tungsten–benzylidyne photoredox complexes

Year: 2019

Type: article

Abstract: -substituent Hammett parameters of the ligands, the models predict energies of frontier orbitals that correlate with redox potentials. The regressive models capture the multivariate character of electron-donating trends as influenced by multiple substituents even for non-aryl ligands, harnessing the breadth of Hammett parameters in a generalized model. We find a tungsten catalyst with tetramethylethylenediamine (tmeda) equatorial ligands and axial methoxyl substituents that should attract significant experimental interest since it is predicted to be highly reducing when photoactivated with visible light. The utilization of Hammett parameters in this study presents a generalizable and compact representation for exploring the effects of ligand substitutions.

Source: Chemical Science

DOI: 10.1039/c9sc02339a

Authors: Alexander M. Chang, Jessica G. Freeze, Victor S. Batista

Institution: Yale University

Language: English

Topic: Radical Photochemical Reactions

Subfield: Organic Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: diamond

Funders: Yale University, National Energy Research Scientific Computing Center, Air Force Office of Scientific Research

Award: G306640760