ArticleScientific data2024
QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states.Digital discovery · 2026Article
- 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
- Dataset exploring the atomic scale structure and ionic dynamics of polyanion sodium cathode materials.Scientific data · 2025Article
- QM9star, two Million DFT-computed Equilibrium Structures for Ions and Radicals with Atomic Information.Scientific data · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Ions and radicals serve as key intermediates in molecular transformation, with their chemical properties being essential for understanding and predicting reaction reactivity and selectivity. In this data descriptor, we report a quantum chemical dataset named QM9star, comprising cations, anions, and radicals. This dataset is derived from the molecular structures of the QM9 dataset, created by removing terminal hydrogens followed by optimization using B3LYP-D3(BJ)/6-311 + G(d,p) level of density functional theory. The QM9star dataset includes approximately 1.9 million cations, anions, and radicals, along with 120 kilo neutral molecules prior to hydrogen removal. Each entry encompasses both molecular and atomic information: representative global properties include orbital energies, vibrational frequencies, etc., while local properties cover aspects such as charges and spin densities at each atomic site. The QM9star dataset not only serves as a comprehensive source of quantum chemical information for intermediates but also offers insights into the principle of atomic property distribution. We anticipate that these data will aid in machine learning studies related to chemical intermediates and contribute to the molecular representation learning.
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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.