  Machine-learning models are increasingly used to predict properties of atoms
in chemical systems. There have been major advances in developing descriptors
and regression frameworks for this task, typically starting from (relatively)
small sets of quantum-mechanical reference data. Larger datasets of this kind
are becoming available, but remain expensive to generate. Here we demonstrate
the use of a large dataset that we have "synthetically" labelled with per-atom
energies from an existing ML potential model. The cheapness of this process,
compared to the quantum-mechanical ground truth, allows us to generate millions
of datapoints, in turn enabling rapid experimentation with atomistic ML models
from the small- to the large-data regime. This approach allows us here to
compare regression frameworks in depth, and to explore visualisation based on
learned representations. We also show that learning synthetic data labels can
be a useful pre-training task for subsequent fine-tuning on small datasets. In
the future, we expect that our open-sourced dataset, and similar ones, will be
useful in rapidly exploring deep-learning models in the limit of abundant
chemical data.
