Data + AI: The core of materials genomic engineering

WANG Hong, XIANG Xiaodong, ZHANG Lanting

Science & Technology Review ›› 2018, Vol. 36 ›› Issue (14) : 15-21.

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Science & Technology Review ›› 2018, Vol. 36 ›› Issue (14) : 15-21. DOI: 10.3981/j.issn.1000-7857.2018.14.003
Scientific Comments

Data + AI: The core of materials genomic engineering

Author information -
1. Materials Genome Initiative Center, Shanghai Jiao Tong University, Shanghai 200240, China;
2. School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
3. Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China

Abstract

The working models of the Materials Genomic Engineering can be roughly classified into those of the experiment-driven, the computation-driven and the data-driven. The last kind of model is consistent with the fourth paradigm of scientific approach of a fundamental change from "trial and error" to "data-intensive". Such a paradigm shift allows one to acquire the composition-structureprocess-performance relationship, as the basis for the rational design of materials, in a faster, cheaper and more accurate way. It represents the core concept and the future direction of the MGI. In this data-centric scientific era, the ability to quickly obtain a large amount of materials data becomes essential. Thus, the "data foundries"-the centralized materials data generation facilities based on high-throughput experiments and high-throughput computations are the key infrastructures for meeting the future data needs. It is contemplated that the data and the artificial intelligence will become the foundation for building the materials science of the future.

Key words

materials genome engineering / data-driven / high-throughput experiments / high-throughput computation

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WANG Hong, XIANG Xiaodong, ZHANG Lanting. Data + AI: The core of materials genomic engineering[J]. Science & Technology Review, 2018, 36(14): 15-21 https://doi.org/10.3981/j.issn.1000-7857.2018.14.003

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