  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.
