PubChemQC B3LYP/6-31G*//PM6 dataset: the Electronic Structures of 86 Million Molecules using B3LYP/6-31G* calculations
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Abstract (arXiv)
This article presents the "PubChemQC B3LYP/6-31G*//PM6" dataset, containing electronic properties of 85,938,443 molecules. It includes orbitals, orbital energies, total energies, dipole moments, and other relevant properties. The dataset encompasses a wide range of molecules, from essential compounds to biomolecules up to 1000 molecular weight, covering 94.0% of the original PubChem Compound catalog (as of August 29, 2016). The electronic properties were calculated using the B3LYP/6-31G* and PM6 methods. The dataset is available in three formats: (i) GAMESS quantum chemistry program files, (ii) selected JSON output files, and (iii) a PostgreSQL database, enabling researchers to query molecular properties. Five sub-datasets offer more specific data. The first two subsets include molecules with C, H, O, and N, under 300 and 500 molecular weight respectively. The third and fourth subsets contain C, H, N, O, P, S, F, and Cl, under 300 and 500 molecular weight respectively. The fifth subset includes C, H, N, O, P, S, F, Cl, Na, K, Mg, and Ca, under 500 molecular weight. Coefficients of determination ranged from 0.892 (CHON500) to 0.803 (whole) for the HOMO-LUMO energy gap. These findings represent extensive investigations and can be utilized for drug discovery, material science, and other applications. The datasets are available under the Creative Commons Attribution 4.0 International license at https://nakatamaho.riken.jp/pubchemqc.riken.jp/b3lyp_pm6_datasets.html.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 7 of its 96 references that are in the map. First posted on arXiv 29 May 2023.
Builds on
7 of 96 references.
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Citing papers per year: 2023: 2; 2024: 5; 2025: 3; 2026: 2.
12 papers.
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- 2024NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Librariescited by 3
- 2024Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learningcited by 3
- 2026Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciencescited by 3
- 2025EDBench: Large-Scale Electron Density Data for Molecular Modelingcited by 2
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- 2025Dual-Axis RCCL: Representation-Complete Convergent Learning for Organic Chemical Spacecited by 0
- 2026Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences15 Jun 2026
- 2026Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing7 Jan 2026
- 2025Dual-Axis RCCL: Representation-Complete Convergent Learning for Organic Chemical Space16 Dec 2025
- 2025EDBench: Large-Scale Electron Density Data for Molecular Modeling14 May 2025
- 2025Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems26 Feb 2025
- 2024Implicit Delta Learning of High Fidelity Neural Network Potentials8 Dec 2024
- 2024OpenQDC: Open Quantum Data Commons29 Nov 2024
- 2024AI-driven inverse design of materials: Past, present and future14 Nov 2024
- 2024Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning14 Oct 2024
- 2024NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries3 Jul 2024
- 2023Gradual Optimization Learning for Conformational Energy Minimization5 Nov 2023
- 2023Role of Structural and Conformational Diversity for Machine Learning Potentials30 Oct 2023