Accurate and rapid predictions with explainable graph neural networks for small high-fidelity bandgap datasets

• Published in: Modelling and simulation in materials science and engineering Vol. 32; no. 3; pp. 35006 - 35020
• Main Authors: Xiao, Jianping, Yang, Li, Wang, Shuqun
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.04.2024
• Subjects: bandgap; explanation; graph neural network; transfer learning
• ISSN: 0965-0393; 1361-651X
• DOI: 10.1088/1361-651X/ad2285

Abstract Accurate and rapid bandgap prediction is a fundamental task in materials science. We propose graph neural networks with transfer learning to overcome the scarcity of training data for high-fidelity bandgap predictions. We also add a perturbation-based component to our framework to improve explainability. The experimental results show that a framework consisting of graph-level pre-training and standard fine-tuning achieves superior performance on all high-fidelity bandgap prediction tasks and training-set sizes. Furthermore, the framework provides a reliable explanation that considers node features together with the graph structure. We also used the framework to screen 105 potential photovoltaic absorber materials.