Issue 32, 2020

Graph convolutional neural networks with global attention for improved materials property prediction

Abstract

The development of an efficient and powerful machine learning (ML) model for materials property prediction (MPP) remains an important challenge in materials science. While various techniques have been proposed to extract physicochemical features in MPP, graph neural networks (GNN) have also shown very strong capability in capturing effective features for high-performance MPP. Nevertheless, current GNN models do not effectively differentiate the contributions from different atoms. In this paper we develop a novel graph neural network model called GATGNN for predicting properties of inorganic materials. GATGNN is characterized by its composition of augmented graph-attention layers (AGAT) and a global attention layer. The application of AGAT layers and global attention layers respectively learn the local relationship among neighboring atoms and overall contribution of the atoms to the material's property; together making our framework achieve considerably better prediction performance on various tested properties. Through extensive experiments, we show that our method is able to outperform existing state-of-the-art GNN models while it can also provide a measurable insight into the correlation between the atoms and their material property. Our code can found on – https://github.com/superlouis/GATGNN.

Graphical abstract: Graph convolutional neural networks with global attention for improved materials property prediction

Article information

Article type
Paper
Submitted
18 3 2020
Accepted
31 7 2020
First published
03 8 2020

Phys. Chem. Chem. Phys., 2020,22, 18141-18148

Author version available

Graph convolutional neural networks with global attention for improved materials property prediction

S. Louis, Y. Zhao, A. Nasiri, X. Wang, Y. Song, F. Liu and J. Hu, Phys. Chem. Chem. Phys., 2020, 22, 18141 DOI: 10.1039/D0CP01474E

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements