Poster
in
Workshop: AI for Accelerated Materials Design (AI4Mat-2023)
AnisoGNN: physics-informed graph neural networks that generalize to anisotropic properties of polycrystals
Guangyu Hu · Marat Latypov
Keywords: [ graph neural networks ] [ microstructure-property relationships ] [ simulations ] [ polycrystals ] [ Graph neural networks ]
We present AnisoGNNs -- graph neural networks (GNNs) that generalize predictions of anisotropic properties of polycrystals in arbitrary testing directions without the need in excessive training data. To this end, we develop GNNs with a physics-inspired combination of node attributes and aggregation function. We demonstrate the excellent generalization capabilities of AnisoGNNs in predicting anisotropic elastic and inelastic properties of two alloys.