# PubChemQC B3LYP/6-31G*//PM6 dataset: the Electronic Structures of 86 Million Molecules using B3LYP/6-31G* calculations

arXiv: https://arxiv.org/abs/2305.18454 · HTML: https://mlsanity.com/paper/2305.18454/

## 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 (most cited first, 7 of 7)

- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Deep Learning Scaling is Predictable, Empirically](https://mlsanity.com/paper/1712.00409/) (2017)
- [Inverse design of 3d molecular structures with conditional generative neural networks](https://mlsanity.com/paper/2109.04824/) (2021)
- [Deep learning for molecular design - a review of the state of the art](https://mlsanity.com/paper/1903.04388/) (2019)
- [GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction](https://mlsanity.com/paper/2212.02229/) (2022)

## Built on by (most cited first, 12 of 12)

- [Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems](https://mlsanity.com/paper/2502.19227/) (2025)
- [AI-driven inverse design of materials: Past, present and future](https://mlsanity.com/paper/2411.09429/) (2024)
- [Gradual Optimization Learning for Conformational Energy Minimization](https://mlsanity.com/paper/2311.06295/) (2023)
- [NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries](https://mlsanity.com/paper/2407.03428/) (2024)
- [Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning](https://mlsanity.com/paper/2410.10118/) (2024)
- [Speaking the Language of Science: Toward a General-Purpose Generative Foundation Model for the Natural Sciences](https://mlsanity.com/paper/2606.16905/) (2026)
- [EDBench: Large-Scale Electron Density Data for Molecular Modeling](https://mlsanity.com/paper/2505.09262/) (2025)
- [Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing](https://mlsanity.com/paper/2601.03774/) (2026)
- [Role of Structural and Conformational Diversity for Machine Learning Potentials](https://mlsanity.com/paper/2311.00862/) (2023)
- [OpenQDC: Open Quantum Data Commons](https://mlsanity.com/paper/2411.19629/) (2024)
- [Implicit Delta Learning of High Fidelity Neural Network Potentials](https://mlsanity.com/paper/2412.06064/) (2024)
- [Dual-Axis RCCL: Representation-Complete Convergent Learning for Organic Chemical Space](https://mlsanity.com/paper/2512.14418/) (2025)

## 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.