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Organic photoredox catalysts for CO 2 reduction: Driving discovery with genetic algorithms.
- Source :
-
The Journal of chemical physics [J Chem Phys] 2022 May 14; Vol. 156 (18), pp. 184109. - Publication Year :
- 2022
-
Abstract
- This work implements a genetic algorithm (GA) to discover organic catalysts for photoredox CO <subscript>2</subscript> 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 <subscript>2</subscript> 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 <subscript>2</subscript> , and excitation energy, we recommend 25 catalysts for further experimental investigation of viability toward photoredox CO <subscript>2</subscript> reduction.
- Subjects :
- Catalysis
Electron Transport
Oxidation-Reduction
Algorithms
Carbon Dioxide
Subjects
Details
- Language :
- English
- ISSN :
- 1089-7690
- Volume :
- 156
- Issue :
- 18
- Database :
- MEDLINE
- Journal :
- The Journal of chemical physics
- Publication Type :
- Academic Journal
- Accession number :
- 35568537
- Full Text :
- https://doi.org/10.1063/5.0088353