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

Abstract

Promising {Delta}-machine learning approaches aim to correct the values of molecular properties obtained with computationally inexpensive methods to the accuracy of higher levels of theory. Training such models requires datasets containing the results of calculations at several different levels of quantum chemical theory. While several large and chemically diverse datasets have been published, studies in many areas require specialized datasets of 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 the results of quantum chemical calculations including properties, geometries, wavefunctions, and electron densities for 3551 molecules including 3417 Cl-PAHs with up to 6 rings and a different number of chlorine atoms in their structure as well as 134 parent polycyclic aromatic hydrocarbons (PAHs). The major part of these molecules have previously not been included in any quantum chemical datasets. The calculations were performed at three different levels of theory including geometry optimization with GFN2-xTB and r2SCAN-3c methods and single-point energy calculation at {omega}B97X-D4/def2-TZVP DFT level. The dataset can be useful to develop and validate the computational, machine learning, or experimental, approaches and study the structure-property relationships for Cl-PAHs.