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.