Machine learning property predictors allow identifying novel materials with desired properties, but it is challenging to explore innovative materials beyond the boundaries of existing data. Here, a meta-learning approach enhances the extrapolative generalization capabilities of neural networks, as demonstrated in predicting the properties of polymeric materials and hybrid organic-inorganic perovskites.
- Kohei Noda
- Araki Wakiuchi
- Ryo Yoshida
