Material Calculation Collaborates with Grain Morphology Knowledge Graph for Material Properties Prediction

Ziwen Pan, Chao Shu, Zhuoran Xin, Cheng Xie, Qi Shen, Yun Yang

Abstract: The study of microstructure of materials is of great significance in the field of materials science. The interdisciplinary cooperation of materials science and computer science makes it more accurate and efficient to explore the relationship between material microstructure and material properties. Machine learning has potential in exploring the relationship between microstructure and properties of materials. This paper proposes adding the morphology features of grains into the construction of grain knowledge graph to enrich the grain information in the graph. First, an autoencoder extracts the grain morphology features and adds them to the grain knowledge graph. Then, the graph convolutional network is used to extract the features of the graph, and the fully connected network is used to predict the properties of the material. Experiments are performed on actual EBSD scanning data. The experimental results show that the proposed method has noticeable improvement over the competing methods.