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PERLA is an ecosystem for perovskite solar cells data built on NOMAD. It provides tools and workflows for extracting, structuring, and analyzing experimental data from the scientific literature.
Getting Started¶
📚 Tutorials¶
Learning-oriented guides
Step-by-step tutorials and interactive Jupyter notebooks demonstrating data queries, analysis workflows, and machine learning applications descripbed in the paper.
📖 How-to Guides¶
Task-oriented instructions
Contribute to the docs, access data in NOMAD, query the API, run extraction tools locally, or contribute data and code to the project.
💡 Explanation¶
Understanding-oriented context
Understand PERLA's repositories, the living database concept, the data validation, and the ecosystem powering the database and automated extraction.
📋 Reference¶
Information-oriented documentation
Technical specifications, data schema details, and bibliographic resources for researchers and developers.
Key Features¶
- Automated Data Extraction: Tools for monitoring literature and extracting device data
- Physics-Based Validation: Quality control through physical consistency checks
- Living Database: Continuously updated dataset in NOMAD
- FAIR Data: Structured, interoperable data following FAIR principles
- Open Source: All tools and data are freely available
Recent Insights from the Living Database¶
With post-2021 literature now flowing continuously into structured form, PERLA enables observation of evolutionary trends such as:
- Performance Evolution: Trajectories in voltage loss reduction over time
- Architectural Changes: Shifts toward inverted (p-i-n) structures with self-assembled monolayers
- Compositional Trends: Transitions from MA-dominated to FA-rich mixed-cation perovskites
- Bandgap Distribution: Evolution toward the 1.50-1.55 eV range
For detailed analysis of these trends, see our recent publication.