Quantum chemical properties of chlorinated polycyclic aromatic hydrocarbons for delta machine learning

Promising Δ-machine learning approaches aim to correct the values of molecular properties calculated using computationally inexpensive approaches to the accuracy of precise methods. Training such models requires data obtained at different levels of quantum chemical theory. While several large and chemically diverse datasets have been published, studies in many areas require specialized datasets for structurally related molecules. Chlorinated polycyclic aromatic hydrocarbons (Cl-PAHs), the products of incomplete combustion of organic substances and materials, are hazardous pollutants with carcinogenic and mutagenic activity. Quantum chemistry methods are important to understand their formation mechanisms and properties. We describe a dataset, PACHQA, containing geometries, physical properties, wavefunctions, and electron densities for 3551 molecules including 3417 Cl-PAHs with up to 6 rings and different number of chlorine atoms, and 134 parent hydrocarbons. Most of them were not included in any published quantum chemical datasets. The calculations were performed at three different levels of theory. The dataset may be useful to develop and validate computational, machine learning, or experimental approaches, and study the structure-property relationships for Cl-PAHs.