  In recent times, transformer networks have achieved state-of-the-art
performance in a wide range of natural language processing tasks. Here we
present a workflow based on the fine-tuning of BERT models for different
downstream tasks, which results in the automated extraction of structured
information from unstructured natural language in scientific literature.
Contrary to existing methods for the automated extraction of structured
compound-property relations from similar sources, our workflow does not rely on
the definition of intricate grammar rules. Hence, it can be adapted to a new
task without requiring extensive implementation efforts and knowledge. We test
our data-extraction workflow by automatically generating a database for Curie
temperatures and one for band gaps. These are then compared with
manually-curated datasets and with those obtained with a state-of-the-art
rule-based method. Furthermore, in order to showcase the practical utility of
the automatically extracted data in a material-design workflow, we employ them
to construct machine-learning models to predict Curie temperatures and band
gaps. In general we find that, although more noisy, automatically extracted
datasets can grow fast in volume and that such volume partially compensates for
the inaccuracy in downstream tasks.
