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MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction by Sanyal, Soumya; Balachandran, Janakiraman; Yadati, Naganand; Kumar, Abhishek; Rajagopalan, Padmini; Sanyal, Suchismita; Talukdar, Partha is a scholarly article available to read on EtoBox.

What is MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction about?

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

Author

Sanyal, Soumya; Balachandran, Janakiraman; Yadati, Naganand; Kumar, Abhishek; Rajagopalan, Padmini; Sanyal, Suchismita; Talukdar, Partha

Published

2018

Language

EN