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

ABSTRACT: The successful application of Hammett parameters as input features for regressive machine learning models is demonstrated and applied to predict energies of frontier orbitals of highly reducing tungsten-benzylidyne complexes of the form W([triple bond, length as m-dash]CArR)L₄X. Using a reference molecular framework and the meta- and para-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 l

SUBMITTER: Chang AM 

PROVIDER: S-EPMC6657405 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature