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arxiv: 2605.14973 · v1 · pith:MOT4FLV4new · submitted 2026-05-14 · ⚛️ physics.chem-ph

THEMol dataset: Torsion, Hessian, and Energy of Molecules

Jiashu Liang , Tianze Zheng , Yu Xia , Xingyuan Xu , Xu Han , Zhi Wang , Siyuan Liu , Ailun Wang
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Yu Liu Shiqian Tan Dongfei Liu Zhichen Pu Yuanheng Wang Qiming Sun Xiaojie Wu Wen Yan
This is my paper

Pith reviewed 2026-06-30 19:31 UTC · model grok-4.3

classification ⚛️ physics.chem-ph
keywords THEMol datasetHessian matricestorsion scansDFT calculationsorganic moleculesmolecular potentialsconformational samplingatomic multipoles
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The pith

THEMol supplies over 3 million Hessians, 100 million torsion scans, and 3 billion DFT calculations for organic molecules up to 50 heavy atoms.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents THEMol as an open-source collection of quantum mechanical properties for closed-shell organic molecules. It assembles more than 3 million relaxed geometries with full Hessian matrices, nearly 100 million constrained geometries from exhaustive torsion scans that include energies and forces, and relaxation trajectories whose total DFT calculations reach about 3 billion. The data cover twelve elements and molecular architectures drawn from drug discovery, electrolytes, and ionic liquids, plus electron-density-derived atomic multipoles. The authors organize the material into five subsets and state that the scale and diversity of second-derivative and conformational information will support construction of accurate, transferable molecular potentials.

Core claim

THEMol is a dataset of optimized geometries, relaxation trajectories, Hessian matrices at those geometries, torsion-scan energies and forces, and MBIS atomic multipoles, all obtained from DFT calculations on closed-shell organic molecules containing up to 50 heavy atoms drawn from twelve elements; the collection comprises a Hessian subset exceeding 3 million entries, a TorsionScan subset approaching 100 million entries, and two relaxation-trajectory subsets whose combined DFT work totals roughly 3 billion calculations.

What carries the argument

The THEMol dataset, which stores relaxed geometries, full Hessian matrices, constrained torsion-scan geometries with energies and forces, full relaxation trajectories, and MBIS multipoles, all generated by DFT for diverse organic molecules.

If this is right

  • Machine-learning force fields can be trained directly on the Hessian matrices to reproduce vibrational frequencies and curvatures at minima.
  • Torsion-scan data enable direct fitting of dihedral parameters that capture both ring and chain conformational preferences.
  • The billions of relaxation-trajectory points supply dense sampling of the potential-energy surface for improving molecular-dynamics accuracy.
  • Atomic multipoles derived from the electron density support development of models that include higher-order electrostatic interactions.
  • The overall scale permits construction of potentials whose transferability can be tested across the stated application domains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The dataset size suggests it could serve as a benchmark collection against which future quantum-chemistry approximations are validated.
  • Because the data stop at closed-shell organic species, extensions to open-shell or metal-containing systems would require new calculations.
  • Combining THEMol with existing smaller datasets could produce hybrid training sets that improve coverage of edge cases.
  • The presence of both Hessian and force data at the same geometries allows direct comparison of first- and second-derivative consistency in learned models.

Load-bearing premise

The sampled molecules and their conformations adequately represent the chemical space needed for drug discovery, electrolytes, and similar applications so that potentials trained on the data will transfer to new molecules.

What would settle it

Training a potential on THEMol and then measuring its error on energies or forces for a test set of molecules that use elements outside the twelve covered or that contain ring systems or functional groups absent from the torsion scans.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript presents THEMol, a large open-source dataset of quantum mechanical properties for closed-shell organic molecules (up to 50 heavy atoms). It comprises five subsets: Hessian (>3 million relaxed geometries with Hessian matrices), TorsionScan (~100 million constrained relaxed geometries with energies and forces), HessianRelax and TorsionScanRelax (together ~3 billion DFT calculations), and MBIS (electron density-derived atomic multipoles via MBIS partitioning). The data emphasize comprehensive sampling across 12 elements and diverse architectures, with exhaustive torsional scans including in-ring and non-ring cases.

Significance. If the generation protocol, validation, and error controls are sound and fully documented, the dataset would be a substantial resource for machine-learned interatomic potentials, supplying rare large-scale Hessian data and extensive conformational sampling at a scale that could improve transferability for applications in drug discovery, electrolytes, and ionic liquids.

major comments (1)
  1. The abstract enumerates subset sizes and contents but supplies no computational details (DFT functional/basis, software, convergence thresholds, or error analysis). This information is load-bearing for assessing whether the stated volumes and properties can be reproduced or trusted; without it the central factual claim cannot be evaluated.
minor comments (1)
  1. The abstract is lengthy; consider moving some descriptive sentences to a dedicated methods or data-generation section for improved readability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and constructive comment. We address the major comment below.

read point-by-point responses

  1. Referee: The abstract enumerates subset sizes and contents but supplies no computational details (DFT functional/basis, software, convergence thresholds, or error analysis). This information is load-bearing for assessing whether the stated volumes and properties can be reproduced or trusted; without it the central factual claim cannot be evaluated.

    Authors: We agree that the abstract would benefit from a concise statement of the core computational parameters. The full manuscript already details the DFT functional, basis set, software package, convergence thresholds, and validation/error controls in the Computational Methods and Validation sections. In the revised manuscript we will add one sentence to the abstract summarizing these parameters (functional, basis, software, and key thresholds) while respecting length constraints. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

This is a dataset release paper whose central claims consist of factual enumerations of generated subset sizes (e.g., >3 million Hessian geometries, ~100 million TorsionScan entries, ~3 billion DFT calculations) and chemical-space coverage. These are presented as direct outcomes of the computational generation process rather than as derived predictions or inferences from equations. No load-bearing derivations, self-citations, ansatzes, or uniqueness theorems appear in the provided text; the argument is self-contained as a description of produced data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced; the work consists of computational data generation and organization.

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discussion (0)

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