The scarcity of experimental training data limits machine learning applications in catalysis research. Here, the authors demonstrate that graph convolutional network models pretrained on a molecular topological index significantly enhance the prediction of catalytic activity, showcasing a promising transfer-learning strategy that leverages self-generated virtual molecular databases.
- Naoki Noto
- Taiki Nagano
- Susumu Saito
