WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets.
Max Pinheiro JúniorShuang ZhangPavlo O DralMario BarbattiPublished in: Scientific data (2023)
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 datasets 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 datasets. 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.
Keyphrases
- molecular dynamics
- density functional theory
- machine learning
- adverse drug
- rna seq
- big data
- artificial intelligence
- multidrug resistant
- human health
- emergency department
- deep learning
- high resolution
- mass spectrometry
- single cell
- risk assessment
- molecular dynamics simulations
- escherichia coli
- staphylococcus aureus
- candida albicans
- psychometric properties