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

Project Page [ OpenReview]

Abstract

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.

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