  Combinatorial and guided screening of materials space with density-functional
theory and related approaches has provided a wealth of hypothetical inorganic
materials, which are increasingly tabulated in open databases. The OPTIMADE API
is a standardised format for representing crystal structures, their measured
and computed properties, and the methods for querying and filtering them from
remote resources. Currently, the OPTIMADE federation spans over 20 data
providers, rendering over 30 million structures accessible in this way, many of
which are novel and have only recently been suggested by machine learning-based
approaches. In this work, we outline our approach to non-exhaustively screen
this dynamic trove of structures for the next-generation of optical materials.
By applying MODNet, a neural network-based model for property prediction,
within a combined active learning and high-throughput computation framework, we
isolate particular structures and chemistries that should be most fruitful for
further theoretical calculations and for experimental study as
high-refractive-index materials. By making explicit use of automated
calculations, federated dataset curation and machine learning, and by releasing
these publicly, the workflows presented here can be periodically re-assessed as
new databases implement OPTIMADE, and new hypothetical materials are suggested.
