ArticleScientific data2023
WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Surface Hopping Nested Instances Training Set for Excited-state Learning.Scientific data · 2025Article
- Vibrational effects on polarizability: insights from normal mode analysis.Scientific reports · 2025Article
- Automated and Efficient Sampling of Chemical Reaction Space.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules.Scientific data · 2025Article
- Beyond chemical structures: lessons and guiding principles for the next generation of molecular databases.Chemical science · 2025Review
- Integrating multiscale and machine learning approaches towards the SAMPL9 logPhysical chemistry chemical physics : PCCP · 2024Article
- From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials.Journal of chemical information and modeling · 2024Article
- Beyond MD17: the reactive xxMD dataset.Scientific data · 2024Article
- MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.Journal of chemical theory and computation · 2024Article
- WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets.Scientific data · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.