The potential of transfer learning as an effective tool for predicting photosensitizer catalytic activity remains underexplored in organic chemistry. Here, the authors apply domain-adaptation-based transfer learning to photocatalysis, sharing knowledge of catalytic activity of photosensitizers among various photoreactions and improving predictions even with small datasets.
- Naoki Noto
- Ryuga Kunisada
- Susumu Saito
