Multi-fidelity physics-guided graph neural networks for predicting structure–property relationships in polycrystalline metals

• Multi-fidelity PGNN is developed for prediction of structure–property linkage. • Introducing physics-guided HP module improves accuracy and sample efficiency. • PGNN rapidly and accurately predicts anisotropic properties and yield loci. • PGNN shows high efficiency and well generalizes across diverse microstructures.