This work implements a genetic algorithm (GA) to discover organic catalysts for photoredox CO2 reduction that are both highly active and resistant to degradation. The LUMO energy of the ground state catalyst is chosen as the activity descriptor and average Mulliken charge on all ring carbons 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 CO2 reduction. A large majority of GA predictions exhibit improved performance relative to experimentally studied o-, m-, and p-terphenyl catalysts. Based on stringent cut-offs imposed on the average charge, barrier to electron transfer to CO2, and excitation energy, we recommend 25 catalysts for further experimental investigation of viability towards photoredox CO2 reduction.
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