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Zhu et al. J. Mater. Inf. 2025, 5, 8 Journal of
DOI: 10.20517/jmi.2024.76
Materials Informatics

Research Article Open Access

An optimized strategy for density prediction of
intermetallics across varied crystal structures via

graph neural network

1
6,*
Dexin Zhu 1,# , Mingshuo Nie 2,3,# , Hong-Hui Wu 1,3,4,* , Chunlei Shang , Jiaming Zhu 3,5,* , Xiaoye Zhou ,
1
1
1,4
1,4
1,4
1
Yuan Zhu , Feiyang Wang , Binbin Wang , Shuize Wang , Junheng Gao , Haitao Zhao , Chaolei
1,4
Zhang , Xinping Mao 1,4
1
Beijing Advanced Innovation Center for Materials Genome Engineering, Research Institute for Carbon Neutrality, University of
Science and Technology Beijing, Beijing 100083, China.
2
Software College, Northeastern University, Shenyang 110000, Liaoning, China.
3
Institute of Materials Intelligent Technology, Liaoning Academy of Materials, Shenyang 110004, Liaoning, China.
4
Institute of Steel Sustainable Technology, Liaoning Academy of Materials, Shenyang 110004, Liaoning, China.
5
School of Civil Engineering, Shandong University, Jinan 250061, Shandong, China.
6
Department of Materials Science and Engineering, Shenzhen MSU-BIT University, Shenzhen 518172, Guangdong, China.
#
Authors contributed equally.
* Correspondence to: Prof. Hong-Hui Wu, Beijing Advanced Innovation Center for Materials Genome Engineering, Research
Institute for Carbon Neutrality, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083,
China. E-mail: wuhonghui@ustb.edu.cn; Prof. Jiaming Zhu, Institute of Materials Intelligent Technology, Liaoning Academy of
Materials, 280 Chuangxin Road, Hunnan District, Shenyang 110004, Liaoning, China. E-mail: zhujiaming@sdu.edu.cn; Prof.
Xiaoye Zhou, Department of Materials Science and Engineering, Shenzhen MSU-BIT University, 1 International University Park
Road, Dayun New Town, Longgang District, Shenzhen 518172, Guangdong, China. E-mail: xiaoye_zhou@smbu.edu.cn
How to cite this article: Zhu, D.; Nie, M.; Wu, H. H.; Shang, C.; Zhu, J.; Zhou, X.; Zhu, Y.; Wang, F.; Wang, B.; Wang, S.; Gao, J.;
Zhao, H.; Zhang, C.; Mao, X. An optimized strategy for density prediction of intermetallics across varied crystal structures via
graph neural network. J. Mater. Inf. 2025, 5, 8. https://dx.doi.org/10.20517/jmi.2024.76
Received: 21 Nov 2024 First Decision: 16 Dec 2024 Revised: 27 Dec 2024 Accepted: 2 Jan 2025 Published: 10 Feb 2025
Academic Editors: Rika Kobayashi, Ming Hu Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang
Abstract
Intermetallic compounds are crucial in modern industry due to their exceptional properties, where density is
identified as a critical parameter determining their potentiality for lightweight applications. In this study, over
7,000 density data points are collected for binary intermetallic compounds from different crystal structures. A new
intermetallics graph neural network (IGNN) model is developed to perform regression and classification tasks for
density prediction. Compared to traditional machine learning models, the IGNN model demonstrated superior
capability in capturing crystal structure and effectively addressing challenges posed by polymorphism. The

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