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/odbx.science.

Serving multiple databases

Materials Cloud uses optimade-python-tools as a library to provide an OPTIMADE API entries to 1) their main databases create with the AiiDA Python framework; and 2) to user-contributed data via the Archive platform. Separate OPTIMADE API apps are started for each database, mounted as separate endpoints to a parent FastAPI instance. For converting the underying data to the OPTIMADE format, the optimade-maker toolkit is used.

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.