This plugin powers the data management infrastructure for the Materials Discovery group at the DTU Nanolab, led by Andrea Crovetto. The group specializes in high-throughput combinatorial synthesis and characterization of thin-film materials for sustainable energy applications.

📄 Paper 🔍 NOMAD Oasis Plugin Repository

Getting Started

📚 Tutorial

Learning-oriented guides

Follow our comprehensive step-by-step guide through a complete combinatorial materials discovery project: from sputtering deposition to data visualization.

Start the tutorial →

📖 How-to Guides

Task-oriented instructions

Practical guides for using the NOMAD Oasis deployment:

Data Upload: - Upload Sputtering Data - Add EDX Measurements - Add Raman Measurements - Add XRD Measurements - Add Ellipsometry Measurements - Add RTP Data - Cleave Libraries

Visualization & Analysis: - Plot Combinatorial EDX Data - Export High-Quality Figures

View all guides →

💡 Explanation

Understanding-oriented context

Conceptual understanding of the plugin:

• Data Model Philosophy
• Materials Discovery Workflow
• Combinatorial Libraries
• Characterization Techniques

Learn more →

📋 Reference

Information-oriented documentation

Complete technical documentation:

• Schema organization
• Entity schemas (samples, substrates, targets, instruments, gases)
• Activity schemas (sputtering, RTP, cleaving, XRD, XPS, EDX, PL, ellipsometry, Raman, RT)

Explore the schema reference →

Research Focus

The Materials Discovery group develops novel semiconductor materials for:

• Photovoltaics: Next-generation solar cell materials including phosphosulfides, thiophosphates, and selenium-based absorbers
• Transparent Conductors: p-type and n-type transparent conducting materials for optoelectronic devices
• Sustainable Materials: Earth-abundant, non-toxic alternatives to conventional semiconductors

Combinatorial Approach

The group employs a combinatorial materials discovery workflow that accelerates materials exploration:

1. Multi-target sputtering creates composition gradient libraries on single substrates
2. Position-based sampling maps specific measurement points across composition space
3. High-throughput characterization measures multiple sample positions in parallel
4. Data-driven analysis identifies promising compositions for further development

This plugin enables FAIR (Findable, Accessible, Interoperable, Reusable) data management for the entire workflow, from synthesis to characterization, ensuring reproducibility and facilitating collaboration.

Citation

If you use this plugin in your research, please cite:

Mittmann, L. A., Nässtrom, H., Bertin, E., Kapoor, S., Diogo, I., Itzhak, A., Dalmonte, G., Thorup Danielsen, R., Skytte, M. E., Von Wenckstern, H., Crovetto, A., & Márquez, J. A. (2026). Digital infrastructure to support daily high-throughput experimental materials research. ChemRxiv. https://doi.org/10.26434/chemrxiv.15000115/v1

BibTeX

@article{mittmann_digital_nodate,
  title   = {Digital infrastructure to support daily high-throughput experimental materials research},
  author  = {Mittmann, Lena A and N{\"a}sstr{\"o}m, Hampus and Bertin, Eug{\`e}ne and Kapoor, Sarthak and Diogo, In{\^e}s and Itzhak, Anat and Dalmonte, Giulia and {Thorup Danielsen}, Rasmus and Skytte, Malthe E. and Von Wenckstern, Holger and Crovetto, Andrea and M{\'a}rquez, Jos{\'e} A},
  journal = {ChemRxiv},
  year    = {2026},
  volume  = {2026},
  number  = {0216},
  doi     = {10.26434/chemrxiv.15000115/v1},
  url     = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000115/v1},
}