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Journal of Shanghai University >

2022 , Vol. 28 >Issue 3: 399 - 412

DOI: https://doi.org/10.12066/j.issn.1007-2861.2388

Data Collection, Database and Data Processing

Database for materials genome engineering

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  • 1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    2. National Supercomputing Center in Wuxi, Wuxi 214072, Jiangsu, China
    3. Center of Materials Informatics and Data Science, Materials Genome Institute, Shanghai University, Shanghai 200444, China
    4. Zhejiang Laboratory, Hangzhou 311100, Zhejiang China

Received date: 2022-03-30

  Online published: 2022-05-27

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Abstract

Materials data are multi-source, heterogeneous, and high-dimensional. Acquiring diverse and complex materials data as well as establishing a dedicated database for materials genome engineering (MGE) is the foundation for realizing data-driven new materials design. Herein, the materials genome database platform is introduced in terms of its system architecture, implementation, and deployment on a supercomputer. It is based on several core technologies, such as normalized representation of materials data, machine-learning modeling and model cross-domain deployment, machine learning under data privacy protection, and a materials database to a knowledge base using a knowledge graph. Finally, based on an anti-perovskite negative expansion material as an example, the entire application process of the MGE database platform from data curation to machine learning modeling followed by inverse design, in addition to a final experimental validation are discussed comprehensively herein.

Key words： material genome engineering; database; machine learning; knowledge graph

Cite this article

YUE Xichao, FENG Yan, LIU Jian, YU Yeyong, XI Kangjie, QIAN Quan . Database for materials genome engineering[J]. Journal of Shanghai University, 2022 , 28(3) : 399 -412 . DOI: 10.12066/j.issn.1007-2861.2388

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