Poster
in
Workshop: AI for Accelerated Materials Design (AI4Mat-2023)
Aniso-GNN: physics-informed graph neural networks that generalize to anisotropic properties of polycrystals
Guangyu Hu · Marat Latypov
Keywords: [ polycrystals ] [ simulations ] [ microstructure-property relationships ] [ graph neural networks ]
In this paper, we present graph neural networks (GNNs) capturing anisotropic properties of polycrystals. Our submission fits the workshop topic of Machine learning algorithms for materials simulation. Our contributions include: (i) GNNs that feature a physics-inspired combination of the aggregation function and node attributes; (ii) case studies demonstrating excellent generalization of our GNNs to predicting anisotropic properties without the need in extensive training datasets.