Exploring the quantum chemical space of small molecules: QM7-X database

Robust and extensive databases of molecular properties are required to enable rational exploration of chemical space. Most databases created so far either include only equilibrium structures of molecules or do not use sufficiently high level of quantum mechanics. Here, we introduce the QM7-X database created with the goal of sampling the vast chemical space for small organic molecules. As basis for QM7-X, we used all molecules within the GDB7 database. All possible enantiomers and diastereomers were also added. Then, to have a sufficient sampling of the potential energy surface, we have considered 100 non-equilibrium conformations around every conformer of a molecule, producing a database of approximately 4.2 million structures in total. Several physicochemical properties were subsequently computed by performing quantum mechanics calculations using FHI-AIMS code at the PBE0-MBD level of theory. As a first attempt for predicting molecular properties, we have applied neural networks in the form of the SchNet package. We also demonstrate that the exploration of the QM7-X chemical space breaks traditional textbook notions of chemical correlations and enables building robust and transferable machine learning models for molecular property prediction.