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.
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More From: Modelling and Simulation in Materials Science and Engineering
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