The NOMAD laboratory: from data sharing to artificial intelligence

Abstract

The Novel Materials Discovery (NOMAD) Laboratory is a user-driven platform for sharing and exploiting computational materials science data. It accounts for the various aspects of data being a crucial raw material and most relevant to accelerate materials research and engineering. NOMAD, with the NOMAD Repository, and its code-independent and normalized form, the NOMAD Archive, comprises the worldwide largest data collection of this field. Based on its findable accessible, interoperable, reusable data infrastructure, various services are offered, comprising advanced visualization, the NOMAD Encyclopedia, and artificial-intelligence tools. The latter are realized in the NOMAD Analytics Toolkit. Prerequisite for all this is the NOMAD metadata, a unique and thorough description of the data, that are produced by all important computer codes of the community. Uploaded data are tagged by a persistent identifier, and users can also request a digital object identifier to make data citable. Developments and advancements of parsers and metadata are organized jointly with users and code developers. In this work, we review the NOMAD concept and implementation, highlight its orthogonality to and synergistic interplay with other data collections, and provide an outlook regarding ongoing and future developments.

Impact of oxidation morphology on reduced graphene oxides upon thermal annealing

“…In particular, for a given spatial distribution of oxidizing agents on the sample, an optimal value of the annealing temperature could be found, yielding a minimal distortion of the plane and a sizeable recovery of the sp 2 carbon domains. Finally, we mention that our simulation data could actually be further integrated into materials databases [44,45], for further use of machine learning algorithms [46][47][48][49] to extrapolate on morphologies and physical properties (electronic and thermal) of a large spectrum of reduced GOs morphologies. This could enable faster access to important information for designing composites with improved performances.…”

Pitfalls of simulation-based machine learning in optoelectronic device design

“…34,35 Another example is the FAIR Data Initiative. 36 Such activities hopefully lead to more reliable models for key material properties relevant to optoelectronic device simulations. High-end models for sophisticated properties such as optical gain and absorption could be calculated separately and then imported into the full device simulation via ANN or other suitable means.…”

Big Data-Driven Materials Science and Its FAIR Data Infrastructure

“…Overcoming the "silo mentality" of computational materials science and the development and implementation of concepts for an extensive data sharing was initiated and achieved by the NOMAD Center of Excellence (NOMAD, Draxl and Scheffler 2019), considering all aspects of what is now called the FAIR data principles (Wilkinson et al 2016): 2 Data are Findable for anyone interested; they are stored in a way that make them easily Accessible; their representation follows accepted standards (Ghiringhelli et al 2016(Ghiringhelli et al , 2017a, and all specifications are open hence data are Interoperable. All of this enables the data to be used for research questions that could be different from their original purpose; hence data are Repurposable.…”