Source-linked AI summary
NOMAD: The FAIR Concept for Big-Data-Driven Materials Science
Claudia Draxl, Matthias Scheffler
TL;DR
NOMAD addresses underused and shallow materials-science data by building infrastructure for FAIR data sharing and analytics. Its repository, archive, visualization tools, and analytics support materials-data mining, prediction of new materials, and identification of properties in known materials, while predictive descriptions remain challenging for many properties.
Problem
Materials-science data are inefficiently exploited, while chemical compound space remains shallow and proper descriptors behind materials properties and functions remain insufficiently established.
Method
NOMAD builds a Repository, develops parsers and a standardized Archive, and provides visualization and analytics tools for computational materials-science data.
Results
NOMAD became the world-wide largest raw-data collection of its kind and its analytics predicted several new quantum spin Hall insulators not included among the calculated materials.
Takeaways & Limitations
NOMAD enables mining Big Data to predict new materials and identify new properties of already known materials.
Takeaways & Limitations
Highly accurate and predictive descriptions remain challenging for many materials properties, and analytics work when enough data are available.
Abstract
from arXiv · showhide
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.
2 Fritz-Haber-Institut der Max-Planck-Gesellschaft Berlin, Germany
NOMAD addresses the underuse and fragmentation of computational materials data by establishing FAIR infrastructure and tools for Open Materials Science. Its repository, archive, visualization systems, and analytics toolkit support data reuse, discovery, and prediction across materials research.
- 2 Fritz-Haber-Institut der Max-Planck-Gesellschaft Berlin, Germany: Computational materials studies often leave most information unused because publications preserve only a small portion of the underlying data.This limits the effective exploitation of computed materials information.
- 2 Fritz-Haber-Institut der Max-Planck-Gesellschaft Berlin, Germany: NOMAD applies FAIR principles by making data Findable, Accessible, Interoperable, and Re-purposable.Data are stored in normalized, code-independent forms so they can support research questions different from those originally intended.
- 2 Fritz-Haber-Institut der Max-Planck-Gesellschaft Berlin, Germany: NOMAD’s vision is to establish data-driven materials research as a fourth paradigm of computational materials science.The approach responds to materials properties whose correlations and patterns are not visible in small datasets.
- The NOMAD Analytics Toolkit: The Analytics Toolkit mines materials data for descriptors, trends, anomalies, and materials maps that guide discovery across multiple application areas.Prototype applications address crystal structures, thermoelectrics, catalysis, optoelectronics, photovoltaics, alloys, and topological insulators.
- The NOMAD Repository: The NOMAD Repository provides an open, community-wide collection spanning different computational codes, retains data free for at least 10 years, and contains several million calculations.It supports organizing, sharing, exchanging, and recalling computational results.
- The NOMAD Archive: The NOMAD Archive and Encyclopedia make millions of calculations accessible through normalized data, search, visualization, and web-based interfaces.Remote visualization enables interactive exploration without specialized hardware or software, including virtual-reality applications.
- The NOMAD Analytics Toolkit: The analytics approach remains constrained because chemical compound space is shallow and many materials properties require enough data for accurate, predictive descriptions.These limitations particularly affect cases requiring highly accurate prediction.
- The NOMAD Analytics Toolkit: Descriptor analysis produced materials maps that revealed mechanisms behind topological transitions and predicted several new quantum spin Hall insulators.The predicted materials were not included in the calculated dataset.
Author biographies
The section combines author biographies with background references on FAIR data sharing and the NOMAD Center of Excellence.
- Author biographies: Claudia Draxl is a professor at Humboldt-Universität and Max-Planck Fellow at the Fritz-Haber-Institut in Berlin.
- Author biographies: Her research covers semiconductors, interfaces, complex alloys, 2D systems, excited states, and data-driven research.
- Author biographies: Matthias Scheffler directs the Fritz Haber Institute and researches physical and chemical properties of bulk materials, surfaces, interfaces, defects, clusters, and nanostructures.
- FAIR data sharing: NOMAD’s repository concept was developed independently and in parallel with the FAIR Guiding Principles, with practically identical substance.
25 A. Ziletti et al., ArXiv:1709.02298v1
This section lists references and web resources associated with materials-data analysis and the NOMAD infrastructure.
- Related references: The references include work on materials informatics, data-driven materials science, and materials-property analysis.
- Related references: Additional citations cover FAIR data discussions, scientific-data publishing, computational methods, and materials-selection frameworks.
- NOMAD resources: The cited resources include the NOMAD Repository, NOMAD Encyclopedia, and NOMAD Analytics Toolkit.