Lightweight and high-precision materials property prediction using pre-trained Graph Neural Networks and its application to a small dataset

Abstract

Large data sets are essential for building deep learning models. However, generating large datasets with higher theoretical levels and larger computational models remains difficult due to the high cost of first-principles calculation. Here, we propose a lightweight and highly accurate machine learning approach using pre-trained Graph Neural Networks (GNNs) for industrially important but difficult to scale models. The proposed method was applied to a small dataset of graphene surface systems containing surface defects, and achieved comparable accuracy with six orders of magnitude and faster learning than when the GNN was trained from scratch.

Keywords

• machine learning
• defect
• graph neural network
• transfer learning
• DFT simulation
• van der Waals