Hammett neural networks: prediction of frontier orbital energies of tungsten-benzylidyne photoredox complexes
2019; 10 (28): 6844–54
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 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.
View details for DOI 10.1039/c9sc02339a
View details for Web of Science ID 000476545100008
View details for PubMedID 31391907
View details for PubMedCentralID PMC6657405
Inverse Design of a Catalyst for Aqueous CO/CO2 Conversion Informed by the Ni-II-Iminothiolate Complex
2018; 57 (24): 15474–80
A computational inverse design method suitable to assist the development and optimization of molecular catalysts is introduced. Catalysts are obtained by continuous optimization of "alchemical" candidates in the vicinity of a reference catalyst with well-defined reaction intermediates and rate-limiting step. A NiII-iminoalkoxylate catalyst for aqueous CO/CO2 conversion is found with improved performance relative to a NiII-iminothiolate reference complex, previously reported as a biomimetic synthetic model of CO dehydroxygenase. Similar energies of other intermediates and transition states along the reaction mechanism show improved scaling relations relative to the reference catalyst. The linear combination of atomic potential tight-binding model Hamiltonian and the limited search of synthetically viable changes in the reference structure enable efficient minimization of the energy barrier for the rate-limiting step (i.e., formation of [LNiII(COOH)]-), bypassing the exponential scaling problem of high-throughput screening techniques. The reported findings demonstrate an inverse design method that could also be implemented with multiple descriptors, including reaction barriers and thermodynamic parameters for reversible reactivity.
View details for DOI 10.1021/acs.inorgchem.8b02799
View details for Web of Science ID 000453938700046
View details for PubMedID 30481007