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

By J. Hoja, Leonardo Medrano Sandonas, Brian G Ernst, Á. Vázquez-Mayagoitia, R. DiStasio, Alexandre Tkatchenko. 2020

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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.

Subfields: Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics, Materials Chemistry

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Citations in the corpus

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules (2020, background): “Modern machine learning models for molecular property prediction typically focus on molecules in equilibrium (e.g. QM9 [13]) or close to the equilibrium (e.g. MD17 [2], ANI-1 [15], QM7-X [7]).”
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties (2020, background): “[23] Johannes Hoja, Leonardo Medrano Sandonas, Brian G Ernst, Alvaro Vazquez-Mayagoitia, Robert A DiStasio Jr, and Alexandre Tkatchenko.”