Description
A fundamental concern in materials science is the pursuit of efficient synthesis protocols to create materials with precise properties in a reproducible manner. Experiments in this area involve a multidimensional parameter space, making analysis challenging and time-consuming. This becomes even more complex when combining datasets from different labs or from different scientists due to the lack of established standards for data models and methods to capture the large number of experimental details, including elaborate workflows and a large diversity of instruments for characterization.
The Electronic Laboratory Notebook (ELN) emerges as the tool of choice for capturing synthesis experiment data, effectively replacing traditional paper-based laboratory notebooks. NOMAD (nomad-lab.eu) offers ELN functionalities, creating a secure environment that safeguards both data and metadata integrity.
Adopting a bottom-up approach, we transition from specific sets of experiments to a generalized description that encapsulates recurring similarities. Envisioning a common data structure as a standard is the next step. For instance, experiments and synthesis inherently share core concepts such as material, measurement, or sample that can be shared among larger communities to enhance the interoperability of data.
In this poster, we shall elucidate how the NOMAD platform can be integrated into the entire research data management life cycle, aligning with FAIR data standards in crystal growth and epitaxy. Using data from Gallium Oxide epitaxy [1], we will showcase the state-of-the-art ELN features for a synthesis process within NOMAD. Our focus will span from the foundational data model concepts to the practical implementation of a use case, towards an automatic workflow that will provide curated data to be used in AI-based analysis.
[1] Chou, T.-S. et al. Machine learning supported analysis of MOVPE grown β-Ga2O3 thin films on sapphire. Journal of Crystal Growth. 2022; 126737.
| Full name | Andrea Albino |
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