ArticleScientific data2019
Energy refinement and analysis of structures in the QM9 database via a highly accurate quantum chemical method.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Learning the One-Electron Reduced Density Matrix at SCF Convergence Thresholds.Journal of chemical theory and computation · 2025Article
- Adapting hybrid density functionals with machine learning.Science advances · 2025Article
- Beyond chemical structures: lessons and guiding principles for the next generation of molecular databases.Chemical science · 2025Review
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
- QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information.Scientific data · 2024Article
- Quantum Chemistry Dataset with Ground- and Excited-state Properties of 450 Kilo Molecules.Scientific data · 2024Article
- MultiXC-QM9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods.Scientific data · 2023Article
- High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions.Scientific data · 2022Article
- Machine Learning Applications for Chemical Reactions.Chemistry, an Asian journal · 2022Review
- VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces.Scientific data · 2022Article
- Machine Learning for Electronically Excited States of Molecules.Chemical reviews · 2021Article
- Physically inspired deep learning of molecular excitations and photoemission spectra.Chemical science · 2021Article
- Energy refinement and analysis of structures in the QM9 database via a highly accurate quantum chemical method.Scientific data · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
A wide variety of data-driven approaches have been introduced in the field of quantum chemistry. To extend the applicable range and improve the prediction power of those approaches, highly accurate quantum chemical benchmarks that cover extremely large chemical spaces are required. Here, we report ~134 k quantum chemical calculations performed with G4MP2, the fourth generation of the G-n series in which second-order perturbation theory is employed. A single composite method calculation executes several low-level calculations to reproduce the results of high-level ab initio calculations with the aim of saving computational costs. Therefore, our database reports the results of the various methods (e.g., density functional theory, Hartree-Fock, Møller-Plesset perturbation theory, and coupled-cluster theory). Additionally, we examined the structure information of both the QM9 and the revised databases via chemical graph analysis. Our database can be applied to refine and improve the quality of data-driven quantum chemical prediction. Furthermore, we reported the raw outputs of all calculations performed in this work for other potential applications.
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Registered trials
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