NOMAD: The FAIR concept for big data-driven materials science

被引：315

作者：

Draxl, Claudia ^{[1
,2
]}

Scheffler, Matthias ^{[1
,2
]}

机构：

[1] Humboldt Univ, Berlin, Germany

[2] Fritz Haber Inst Berlin, Berlin, Germany

来源：

MRS BULLETIN | 2018年 / 43卷 / 09期

基金：

欧盟地平线“2020”;

关键词：

data repositories; metadata; data sharing; machine learning; artificial intelligence;

D O I：

10.1557/mrs.2018.208

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Data are a crucial raw material of this century. The amount of data that have been created in materials science thus far and that continues to be created every day is immense. Without a proper infrastructure that allows for collecting and sharing data, the envisioned success of big data-driven materials science will be hampered. For the field of computational materials science, the NOMAD (Novel Materials Discovery) Center of Excellence (CoE) has changed the scientific culture toward comprehensive and findable, accessible, interoperable, and reusable (FAIR) data, opening new avenues for mining materials science big data. Novel data-analytics concepts and tools turn data into knowledge and help in the prediction of new materials and in the identification of new properties of already known materials.

引用

页码：676 / 682

页数：7

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