CHAOS -- A Consistent Large-scale Database for Sigma-Profiles and Other Molecular Descriptors

TL;DR

CHAOS is a large, consistent database of sigma-profiles and quantum-chemical descriptors for over 53,000 molecules, enabling advanced modeling in chemistry and materials science.

Contribution

It introduces a comprehensive, standardized, and publicly available database of molecular descriptors, significantly expanding existing resources for research and modeling.

Findings

Provides sigma-profiles for 53,091 molecules, over ten times more than previous databases.

Includes diverse quantum-chemical observables like IR spectra, NMR tensors, and thermodynamic data.

Ensures data consistency through a standardized computational workflow.

Abstract

Sigma-profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, existing sigma-profile libraries are limited in size and inconsistent in quality, which restricts their utility. In this work, we introduce CHAOS (Computed High-Accuracy Observables and Sigma Profiles), a large-scale and internally consistent database providing sigma-profiles for 53091 molecules, along with additional quantum-chemical observables including gas-phase geometries, single-point conductor-like polarizable continuum (C-PCM) data, infrared spectra, ideal-gas heat capacities and entropies, and atomic orbital nuclear magnetic resonance (NMR) shielding tensors. All data were generated using a standardized quantum-chemical workflow based on an wB97X-D/def2-TZVP level of theory. The CHAOS database covers molecules composed of a diverse set of elements, with molecular masses up to 400 amu and dipole moments up to 15 D, and is freely available on Zenodo under an open license. It extends the number of molecules for which sigma-profiles are publicly available by more than an order of magnitude and systematically links them to a broad range of other quantum-chemical molecular descriptors. CHAOS provides a comprehensive and consistent foundation for developing models of molecular and thermodynamic properties -- both physics-based and machine-learning approaches -- across chemistry, chemical engineering, and materials science, greatly extending the possibilities and the available quantum-chemical data basis.