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

ABSTRACT: 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.
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.
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
http://arxiv.org/abs/2209.10702v2