  One of the grand challenges in modern theoretical chemistry is designing and
implementing approximations that expedite ab initio methods without loss of
accuracy. Machine learning (ML), in particular neural networks, are emerging as
a powerful approach to constructing various forms of transferable atomistic
potentials. They have been successfully applied in a variety of applications in
chemistry, biology, catalysis, and solid-state physics. However, these models
are heavily dependent on the quality and quantity of data used in their
fitting. Fitting highly flexible ML potentials comes at a cost: a vast amount
of reference data is required to properly train these models. We address this
need by providing access to a large computational DFT database, which consists
of 20M conformations for 57,454 small organic molecules. We believe it will
become a new standard benchmark for comparison of current and future methods in
the ML potential community.
