  Machine learning (ML) based interatomic potentials have transformed the field
of atomistic materials modelling. However, ML potentials depend critically on
the quality and quantity of quantum-mechanical reference data with which they
are trained, and therefore developing datasets and training pipelines is
becoming an increasingly central challenge. Leveraging the idea of "synthetic"
(artificial) data that is common in other areas of ML research, we here show
that synthetic atomistic data, themselves obtained at scale with an existing ML
potential, constitute a useful pre-training task for neural-network interatomic
potential models. Once pre-trained with a large synthetic dataset, these models
can be fine-tuned on a much smaller, quantum-mechanical one, improving
numerical accuracy and stability in computational practice. We demonstrate
feasibility for a series of equivariant graph-neural-network potentials for
carbon, and we carry out initial experiments to test the limits of the
approach.
