The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding

Sbailò L, Fekete Á, Ghiringhelli LM, Scheffler M (2022)

Publication Type: Journal article

Publication year: 2022

Journal

npj Computational Materials Nature Research

Book Volume: 8

Article Number: 250

Journal Issue: 1

DOI: 10.1038/s41524-022-00935-z

Abstract

We present the Novel-Materials-Discovery (NOMAD) Artificial-Intelligence (AI) Toolkit, a web-browser-based infrastructure for the interactive AI-based analysis of materials-science findable, accessible, interoperable, and reusable (FAIR) data. The AI Toolkit readily operates on the FAIR data stored in the central server of the NOMAD Archive, the largest database of materials-science data worldwide, as well as locally stored, users’ owned data. The NOMAD Oasis, a local, stand-alone server can be also used to run the AI Toolkit. By using Jupyter notebooks that run in a web-browser, the NOMAD data can be queried and accessed; data mining, machine learning, and other AI techniques can be then applied to analyze them. This infrastructure brings the concept of reproducibility in materials science to the next level, by allowing researchers to share not only the data contributing to their scientific publications, but also all the developed methods and analytics tools. Besides reproducing published results, users of the NOMAD AI toolkit can modify the Jupyter notebooks toward their own research work.

Authors with CRIS profile

Luca Ghiringhelli

Involved external institutions

Humboldt-Universität zu Berlin DE Germany (DE) Fritz-Haber-Institut der Max-Planck-Gesellschaft (FHI) DE Germany (DE)

How to cite

APA:

Sbailò, L., Fekete, Á., Ghiringhelli, L.M., & Scheffler, M. (2022). The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding. npj Computational Materials, 8(1). https://doi.org/10.1038/s41524-022-00935-z

MLA:

Sbailò, Luigi, et al. "The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding." npj Computational Materials 8.1 (2022).

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