THEMol dataset: Torsion, Hessian, and Energy of Molecules

TL;DR

THEMol is a comprehensive open-source dataset of quantum mechanical properties for organic molecules, including geometries, Hessians, torsion scans, and electron densities, designed to advance molecular potential modeling.

Contribution

It introduces a large, diverse dataset with extensive conformational sampling and Hessian information, filling a gap in resources for molecular potential development.

Findings

Contains over 3 million Hessian matrices at relaxed geometries.

Includes nearly 100 million constrained relaxed geometries with energies and forces.

Encompasses about 3 billion DFT calculations across diverse molecular architectures.

Abstract

We present THEMol (Torsion, Hessian, Energy of Molecules), a massive open-source collection of quantum mechanical properties tailored for closed-shell organic molecules, with up to 50 heavy atoms. THEMol includes a Hessian subset with more than 3 million relaxed geometries with Hessian matrices, a TorsionScan subset with nearly 100 million constrained relaxed geometries with energies and forces, and relaxation-trajectory subsets (HessianRelax and TorsionScanRelax) that together comprise about 3 billion DFT calculations. The chemical space sampling is comprehensive, spanning twelve essential elements and diverse molecular architectures relevant to drug discovery, electrolytes, ionic liquids, and beyond. The dataset also features exhaustive conformational sampling through the TorsionScan and TorsionScanRelax subsets, including comprehensive in-ring and non-ring torsional scans. Furthermore, it contains an extensive library of Hessian matrices, computed at relaxed geometries, to capture critical second-derivative information of the potential energy landscape. Additionally, we supply electron density-derived atomic multipoles computed via the Minimal Basis Iterative Stockholder partition scheme. Organized into five distinct subsets (Hessian, TorsionScan, HessianRelax, TorsionScanRelax, and MBIS), the data encompasses optimized geometries, relaxation trajectories, and derived molecular properties. We anticipate that this massive and diverse dataset will significantly empower the development of highly accurate and transferable molecular potentials.