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CHAOS - A Large-scale Database for σ-Profiles and Other Molecular Descriptors.

Dominik Gond1, Justus Arweiler1, Thomas Specht1

  • 1Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, 67663 Kaiserslautern, Germany.

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|April 2, 2026
PubMed
Summary
This summary is machine-generated.

A new database, CHAOS, offers over 53,000 computed molecular sigma-profiles and quantum-chemical data. This large, consistent dataset aids solvent selection, thermodynamic modeling, and data-driven molecular design for various scientific fields.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Engineering

Background:

  • Sigma-profiles are crucial molecular descriptors for applications like solvent selection and thermodynamic modeling.
  • Existing sigma-profile libraries are fragmented and inconsistent due to varied computational methods, limiting their utility.
  • A need exists for a large-scale, internally consistent database of sigma-profiles and related quantum-chemical data.

Purpose of the Study:

  • To introduce CHAOS (Computed High-Accuracy Observables and Sigma Profiles), a novel, extensive, and consistent database of molecular sigma-profiles.
  • To provide a comprehensive set of quantum-chemical observables alongside sigma-profiles for a vast number of molecules.
  • To facilitate advancements in data-driven molecular design and thermodynamic modeling across scientific disciplines.

Main Methods:

  • Generated sigma-profiles and other quantum-chemical observables for 53,091 molecules using a standardized workflow.
  • Employed the ωB97X-D/def2-TZVP level of theory for all quantum-chemical calculations.
  • Included gas-phase geometries, C-PCM data, IR spectra, thermodynamic properties, and NMR shielding tensors.

Main Results:

  • The CHAOS database contains 53,091 molecules with computed sigma-profiles and diverse quantum-chemical data.
  • Data generation utilized a consistent, high-accuracy quantum-chemical workflow.
  • The database covers a wide range of molecular properties (mass up to 400 amu, dipole moments up to 15 D) and elements.

Conclusions:

  • CHAOS significantly expands the availability of public sigma-profile data by over an order of magnitude.
  • The database provides a unified and consistent foundation for developing physics-based and machine-learning models.
  • CHAOS empowers research in chemistry, chemical engineering, and materials science by offering a rich quantum-chemical data basis.