  Developing accurate, transferable and computationally inexpensive machine
learning models can rapidly accelerate the discovery and development of new
materials. Some of the major challenges involved in developing such models are,
(i) limited availability of materials data as compared to other fields, (ii)
lack of universal descriptor of materials to predict its various properties.
The limited availability of materials data can be addressed through transfer
learning, while the generic representation was recently addressed by Xie and
Grossman [1], where they developed a crystal graph convolutional neural network
(CGCNN) that provides a unified representation of crystals. In this work, we
develop a new model (MT-CGCNN) by integrating CGCNN with transfer learning
based on multi-task (MT) learning. We demonstrate the effectiveness of MT-CGCNN
by simultaneous prediction of various material properties such as Formation
Energy, Band Gap and Fermi Energy for a wide range of inorganic crystals (46774
materials). MT-CGCNN is able to reduce the test error when employed on
correlated properties by upto 8%. The model prediction has lower test error
compared to CGCNN, even when the training data is reduced by 10%. We also
demonstrate our model's better performance through prediction of end user
scenario related to metal/non-metal classification. These results encourage
further development of machine learning approaches which leverage multi-task
learning to address the aforementioned challenges in the discovery of new
materials. We make MT-CGCNN's source code available to encourage reproducible
research.
