ArticleScientific data2024
Quantum Chemistry Dataset with Ground- and Excited-state Properties of 450 Kilo Molecules.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Accelerating Electrolyte Discovery Using Generative Models and Molecular Dynamics Simulations.ACS omega · 2026Article
- Transfer learning of GW Bethe-Salpeter equation excitation energies.Chemical science · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- qsGW quasiparticle and GW-BSE excitation energies of 133,885 molecules.Scientific data · 2026Article
- Predicting SJournal of chemical theory and computation · 2026Article
- tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes.Journal of chemical information and modeling · 2025Article
- Surface Hopping Nested Instances Training Set for Excited-state Learning.Scientific data · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Due to rapid advancements in deep learning techniques, the demand for large-volume high-quality datasets grows significantly in chemical research. We developed a quantum-chemistry database that includes 443,106 small organic molecules with sizes up to 10 heavy atoms including C, N, O, and F. Ground-state geometry optimizations and frequency calculations of all compounds were performed at the B3LYP/6-31G* level with the BJD3 dispersion correction, while the excited-state single-point calculations were conducted at the ωB97X-D/6-31G* level. Totally twenty-seven molecular properties, such as geometric, thermodynamic, electronic and energetic properties, were gathered from these calculations. Meanwhile, we also established a comprehensive protocol for the construction of a high-volume quantum-chemistry dataset. Our QCDGE (Quantum Chemistry Dataset with Ground- and Excited-State Properties) dataset contains a substantial volume of data, exhibits high chemical diversity, and most importantly includes excited-state information. This dataset, along with its construction protocol, is expected to have a significant impact on the broad applications of machine learning studies across different fields of chemistry, especially in the area of excited-state research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.