QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules

TL;DR

QM7-X is a large, detailed dataset of quantum-mechanical properties for nearly 4.2 million small organic molecules, including equilibrium and non-equilibrium structures, designed to advance machine learning in chemical space exploration.

Contribution

This work introduces QM7-X, a comprehensive, systematically sampled dataset of quantum properties for small organic molecules, covering diverse structures and conformations at a high level of theory.

Findings

Contains 42 physicochemical properties for 4.2 million structures

Includes equilibrium and non-equilibrium molecular conformations

Provides data at the PBE0+MBD quantum-mechanical level

Abstract

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for $\approx$ 4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures - comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-/trans- and conformational isomers) - as well as 100 non-equilibrium structural variations thereof to reach a total of $\approx$ 4.2 M molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly-converged dataset of quantum-mechanically computed physicochemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties.