| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Polycrystal Graph Neural Network |
| Author(s) |
Minyi Dai, Mehmet Furkan Demirel, Xuanhan Liu, Yingyu Liang, Jia-Mian Hu |
| On-Site Speaker (Planned) |
Minyi Dai |
| Abstract Scope |
Graph Neural Network (GNN) has recently emerged as a powerful machine learning model for predicting the properties of molecular and crystal structures, but its application to 3D, topologically complex polycrystalline microstructures still remains scarce. Here, we develop a Polycrystal Graph Neural Network (PGNN) model that permits an accurate prediction of the properties of polycrystalline microstructures by considering the physical features and interactions of both grain and grain boundaries. Trained with 4000 data points, our PGNN model achieves a property prediction error of ~1.5%, which is significantly lower than baseline machine learning models such as ResNet (error ~4%). We also show that such trained PGNN model can be transferred to accelerate and improve the prediction of other physical properties with smaller available data. Our accurate, and transferable PGNN model is well suited for harnessing large-scale datasets of 3D polycrystalline microstructures, which is crucial for realizing accelerated design of polycrystalline materials. |