  The use of machine learning is becoming increasingly common in computational
materials science. To build effective models of the chemistry of materials,
useful machine-based representations of atoms and their compounds are required.
We derive distributed representations of compounds from their chemical formulas
only, via pooling operations of distributed representations of atoms. These
compound representations are evaluated on ten different tasks, such as the
prediction of formation energy and band gap, and are found to be competitive
with existing benchmarks that make use of structure, and even superior in cases
where only composition is available. Finally, we introduce a new approach for
learning distributed representations of atoms, named SkipAtom, which makes use
of the growing information in materials structure databases.
