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

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials