Transfer learning for materials informatics using crystal graph convolutional neural network - Lee, Joohwi; Asahi, Ryoji (Computational materials science, 2021)
[Display omitted] •Transfer learning (TL) is re-using knowledge accumulated in big pretrained model.•We apply TL to crystal graph convolutional neural network (CGCNN).•TL improves predictions of various target properties suffering from their small data.•TL becomes powerful when bigger pretrained model is employed.•TL becomes powerful when pretrained and target models are strongly correlated. For successful applications of machine learning in materials informatics, it is necessary to overcome the inaccuracy of predictions ascribed to insufficient amount of data. In this study, we propose a transfer learning using a crystal graph convolutional neural network (TL-CGCNN). Herein, TL-CGCNN is pretrained with big data such as formation energies for crystal structures, and then used for predicting target properties with relatively small data. We confirm that TL-CGCNN can improve predictions of various properties such as bulk moduli, dielectric constants, and quasiparticle band gaps, which are computationally demanding, to construct big data for materials. Moreover, we quantitatively observe that the prediction of properties in target models via TL-CGCNN becomes more accurate with an increase in size of training dataset in pretrained models. Finally, we confirm that TL-CGCNN is superior to other regression methods in the predictions of target properties, which suffer from small amount of data. Therefore, we conclude that TL-CGCNN is promising along with compiling big data for materials that are easy to accumulate and relevant to the target properties.