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Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab
Aliaksandr V Yakutovich1,2, Daniel Hollas3, Edan Bainglass4
1Nanotech@Surfaces Laboratory, Empa-Swiss Federal Laboratories for Materials Science and Technology 8600 Dübendorf Switzerland carlo.pignedoli@empa.ch.
Digital Discovery
|May 13, 2026
Summary
AiiDAlab simplifies complex computational research workflows for scientists. This Jupyter-based platform automates simulations and data analysis, enhancing reproducibility and collaboration across disciplines.
Area of Science:
- Computational Science
- Scientific Computing
- Data Management
Background:
- Modern research relies on complex computational workflows requiring significant technical expertise.
- Managing interdependent simulations and parallel code execution poses challenges for researchers.
- Ensuring reproducibility and provenance tracking is crucial for scientific integrity.
Purpose of the Study:
- To present AiiDAlab, a web platform simplifying computational workflow management.
- To highlight AiiDAlab's evolution beyond computational materials science.
- To showcase advancements in ELN integration, large-scale facility adoption, and educational applications.
Main Methods:
- Development of AiiDAlab, a Jupyter-based web platform.
- Leveraging the AiiDA computational infrastructure for workflow automation.
- Creation of open-source, user-friendly applications for scientific workflows.
Main Results:
- AiiDAlab enables scientists to set up, execute, and analyze workflows without deep technical knowledge.
- The platform now integrates with electronic laboratory notebooks for FAIR data management.
- AiiDAlab is adopted in large-scale facilities and educational settings, expanding its reach.
Conclusions:
- AiiDAlab matures into a versatile platform for automating and managing scientific research.
- Recent developments enhance data management, security, and accessibility for diverse scientific communities.
- AiiDAlab accelerates scientific discovery and fosters interdisciplinary collaboration through simplified computational workflows.