NOMAD: The FAIR Concept for Big-Data-Driven Materials Science

TL;DR

The paper discusses the NOMAD initiative, which promotes FAIR data sharing in materials science to enable effective big data analytics and accelerate materials discovery.

Contribution

It introduces the NOMAD Center of Excellence and its infrastructure that fosters FAIR data sharing and novel analytics in computational materials science.

Findings

Established a comprehensive FAIR data infrastructure.

Enabled new data-driven materials discovery approaches.

Facilitated sharing and mining of large materials datasets.

Abstract

Data is a crucial raw material of this century, and the amount of data that has been created in materials science in recent years and is being created every new day is immense. Without a proper infrastructure that allows for collecting and sharing data (including the original data), the envisioned success of materials science and, in particular, 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 towards a comprehensive and FAIR data sharing, opening new avenues for mining Big-Data of materials science. Novel data-analytics concepts and tools turn data into knowledge and help the prediction of new materials or the identification of new properties of already known materials.