  Machine learning potentials are an important tool for molecular simulation,
but their development is held back by a shortage of high quality datasets to
train them on. We describe the SPICE dataset, a new quantum chemistry dataset
for training potentials relevant to simulating drug-like small molecules
interacting with proteins. It contains over 1.1 million conformations for a
diverse set of small molecules, dimers, dipeptides, and solvated amino acids.
It includes 15 elements, charged and uncharged molecules, and a wide range of
covalent and non-covalent interactions. It provides both forces and energies
calculated at the {\omega}B97M-D3(BJ)/def2-TZVPPD level of theory, along with
other useful quantities such as multipole moments and bond orders. We train a
set of machine learning potentials on it and demonstrate that they can achieve
chemical accuracy across a broad region of chemical space. It can serve as a
valuable resource for the creation of transferable, ready to use potential
functions for use in molecular simulations.

Chemical Physics;Machine Learning;Biomolecules