Transfer learning strategies are useful to increase the accuracy of data-driven predictions in low-data regimes. Here the authors present a hybrid framework integrating transfer learning and expert knowledge to predict carrier mobility of 2D materials from bulk properties.
- Xinyu Chen
- Shuaihua Lu
- Jinlan Wang
