WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets

19 September 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Multidimensional surfaces of quantum chemical properties such as potential energies and dipole moments are common targets for machine learning, requiring the development of robust and diverse databases extensively exploring molecular configurational spaces. Here we composed the WS22 database covering several quantum mechanical (QM) properties (including potential energies, forces, dipole moments, polarizabilities, HOMO, and LUMO energies) for ten flexible organic molecules of increasing complexity and with up to 22 atoms. This database consists of 1.18~million equilibrium and non-equilibrium geometries carefully sampled from Wigner distributions centered at different equilibrium conformations (either at the ground or excited electronic states) and further augmented with interpolated structures. The diversity of our data sets is demonstrated by visualizing the geometries distribution with dimensionality reduction as well as via comparison of statistical features of the QM properties with those available in existing data sets. Our sampling targets broader quantum mechanical distribution of the configurational space than provided by commonly used sampling through classical molecular dynamics, upping the challenge for machine learning models.


molecular structures
conformational space
density functional theory
potential energy surfaces
machine learning potential

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