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Title: Organic photoredox catalysts for CO 2 reduction: Driving discovery with genetic algorithms
This work implements a genetic algorithm (GA) to discover organic catalysts for photoredox CO 2 reduction that are both highly active and resistant to degradation. The lowest unoccupied molecular orbital energy of the ground state catalyst is chosen as the activity descriptor and the average Mulliken charge on all ring carbons is chosen as the descriptor for resistance to degradation via carboxylation (both obtained using density functional theory) to construct the fitness function of the GA. We combine the results of multiple GA runs, each based on different relative weighting of the two descriptors, and rigorously assess GA performance by calculating electron transfer barriers to CO 2 reduction. A large majority of GA predictions exhibit improved performance relative to experimentally studied o-, m-, and p-terphenyl catalysts. Based on stringent cutoffs imposed on the average charge, barrier to electron transfer to CO 2 , and excitation energy, we recommend 25 catalysts for further experimental investigation of viability toward photoredox CO 2 reduction.  more » « less
Award ID(s):
2102044
NSF-PAR ID:
10345298
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
The Journal of Chemical Physics
Volume:
156
Issue:
18
ISSN:
0021-9606
Page Range / eLocation ID:
184109
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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