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Example use cases

Serving a single database

The Materials Project uses optimade-python-tools alongside their existing API and MongoDB database, providing OPTIMADE-compliant access to highly-curated density-functional theory calculations across all known inorganic materials.

optimade-python-tools handles filter parsing, database query generation and response validation by running the reference server implementation with minimal configuration.

odbx, a small database of results from crystal structure prediction calculations, follows a similar approach. This implementation is open source, available on GitHub at ml-evs/

Serving multiple databases

Materials Cloud uses optimade-python-tools as a library to provide an OPTIMADE API entry to archived computational materials studies, created with the AiiDA Python framework and published through their archive. In this case, each individual study and archive entry has its own database and separate API entry. The Python classes within the optimade package have been extended to make use of AiiDA and its underlying PostgreSQL storage engine.

Details of this implementation can be found on GitHub at aiidateam/aiida-optimade.

Extending an existing API

NOMAD uses optimade-python-tools as a library to add OPTIMADE API endpoints to an existing web app. Their implementation uses the Elasticsearch database backend to filter on millions of structures from aggregated first-principles calculations provided by their users and partners. NOMAD also uses the package to implement a GUI search bar that accepts the OPTIMADE filter language. NOMAD uses the release versions of the optimade-python-tools package, performing all customisation via configuration and sub-classing. The NOMAD OPTIMADE API implementation is available in the NOMAD FAIR GitLab repository.

This use case is demonstrated in the example Integrate OPTIMADE with an existing web application.