AnisoGNN: Graph neural networks generalizing to anisotropic properties of polycrystals

Guangyu Hu,Marat I. Latypov

Computational Materials Science(2024)

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摘要
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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关键词
Graph neural networks,Polycrystals,Computational homogenization,Mechanical properties
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