ArticleScientific data2025
Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Exploring the chemical space of transition-metal cluster thermodynamics via automated first-principles calculations and machine learning.Nature communications · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials.Scientific data · 2025Article
- Accurate and Efficient Phonon Calculations in Molecular Crystals via Minimal Molecular Displacements.Journal of chemical theory and computation · 2025Article
- Does Hessian Data Improve the Performance of Machine Learning Potentials?Journal of chemical theory and computation · 2025Article
- Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents.Scientific data · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface. We present Hessian QM9, the first database of equilibrium configurations and numerical Hessian matrices, consisting of 41,645 molecules from the QM9 dataset at the ωB97x/6-31G* level. Molecular Hessians were calculated in vacuum, as well as water, tetrahydrofuran, and toluene using an implicit solvation model. To demonstrate the utility of this dataset, we show that incorporating second derivatives of the potential energy surface into the loss function of a MLIP significantly improves the prediction of vibrational frequencies in all solvent environments, thus making this dataset extremely useful for studying organic molecules in realistic solvent environments for experimental characterization.
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Registered trials
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