材料基因组工程专用数据库

doi:10.12066/j.issn.1007-2861.2388

Abstract

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

CLC Number:

TP 391

YUE Xichao, FENG Yan, LIU Jian, YU Yeyong, XI Kangjie, QIAN Quan. Database for materials genome engineering[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(3): 399-412.

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