@inproceedings{
hu2023anisognn,
title={Aniso{GNN}: physics-informed graph neural networks that generalize to anisotropic properties of polycrystals},
author={Guangyu Hu and Marat Latypov},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=LkRfodp4tt}
}
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.
AnisoGNN: physics-informed graph neural networks that generalize to anisotropic properties of polycrystals
Guangyu Hu Marat Latypov