  Machine Learning (ML) has the potential to accelerate discovery of new
materials and shed light on useful properties of existing materials. A key
difficulty when applying ML in Materials Science is that experimental datasets
of material properties tend to be small. In this work we show how material
descriptors can be learned from the structures present in large scale datasets
of material simulations; and how these descriptors can be used to improve the
prediction of an experimental property, the energy of formation of a solid. The
material descriptors are learned by training a Graph Neural Network to regress
simulated formation energies from a material's atomistic structure. Using these
learned features for experimental property predictions outperforms existing
methods that are based solely on chemical composition. Moreover, we find that
the advantage of our approach increases as the generalization requirements of
the task are made more stringent, for example when limiting the amount of
training data or when generalizing to unseen chemical spaces.
