Designing organic photoredox catalysts as a fragment-based puzzle solved by reinforcement learning
Reinforcement learning (RL) is a promising strategy for identifying optimal solutions in vast search spaces. We have developed a framework in which the design of organic photoredox catalysts (OPCs) is deliberately formulated as a fragment-based puzzle that is well suited to RL. In this framework, an RL agent sequential...
Naoki Noto, Mayu Hirakata, Mikito Fujinami et al.
· ChemRxiv · 0 citations