Organic photoredox catalysts for CO reduction: Driving discovery with genetic algorithms.

This work implements a genetic algorithm (GA) to discover organic catalysts for photoredox CO 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 reduction.

Photoredox Chemistry with Organic Catalysts: Role of Computational Methods.

Organic catalysts have the potential to carry out a wide range of otherwise thermally inaccessible reactions via photoredox routes. Early demonstrated successes of organic photoredox catalysts include one-electron CO reduction and H generation via water splitting. Photoredox systems are challenging to study and design owing to the sheer number and diversity of phenomena involved, including light absorption, emission, intersystem crossing, partial or complete charge transfer, and bond breaking or formation.