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Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials

Engineering and Technology

Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials

M. Dai, M. F. Demirel, et al.

This innovative research by Minyi Dai, Mehmet F. Demirel, Yingyu Liang, and Jia-Mian Hu introduces a groundbreaking graph neural network model that predicts the properties of polycrystalline materials with remarkable accuracy. Leveraging the magnetostriction of Tb0.3Dy0.7Fe2 alloys, this model provides insights into the physical interactions among grains and highlights the significance of each grain's features.

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Playback language: English
Abstract
This paper develops a graph neural network (GNN) model to predict the properties of polycrystalline materials by considering the physical interactions among neighboring grains. Using the magnetostriction of Tb0.3Dy0.7Fe2 alloys, the model achieves a low prediction error (~10%) across diverse microstructures and quantifies the importance of each grain's features. This microstructure-graph-based GNN model enables accurate and interpretable predictions.
Publisher
npj Computational Materials
Published On
Jul 09, 2021
Authors
Minyi Dai, Mehmet F. Demirel, Yingyu Liang, Jia-Mian Hu
Tags
graph neural network
polycrystalline materials
magnetostriction
Tb0.3Dy0.7Fe2 alloys
microstructure
prediction model
material properties

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