  We introduce a new molecular dataset, named Alchemy, for developing machine
learning models useful in chemistry and material science. As of June 20th 2019,
the dataset comprises of 12 quantum mechanical properties of 119,487 organic
molecules with up to 14 heavy atoms, sampled from the GDB MedChem database. The
Alchemy dataset expands the volume and diversity of existing molecular
datasets. Our extensive benchmarks of the state-of-the-art graph neural network
models on Alchemy clearly manifest the usefulness of new data in validating and
developing machine learning models for chemistry and material science. We
further launch a contest to attract attentions from researchers in the related
fields. More details can be found on the contest website
\footnote{https://alchemy.tencent.com}. At the time of benchamrking experiment,
we have generated 119,487 molecules in our Alchemy dataset. More molecular
samples are generated since then. Hence, we provide a list of molecules used in
the reported benchmarks.
