Artificial Intelligence Modeling of Materials’ Bulk Chemical and Physical Properties

Energies of the atomic and molecular orbitals belonging to one and two atom systems from the fourth and fifth periods of the periodic table have been calculated using ab initio quantum mechanical calculations. The energies of selected occupied and unoccupied orbitals surrounding the highest occupied and lowest unoccupied orbitals (HOMOs and LUMOs) of each system were selected and used as input for our artificial intelligence (AI) software. Using the AI software, correlations between orbital parameters and selected chemical and physical properties of bulk materials composed of these elements were established. Using these correlations, the materials’ bulk properties were predicted. The Q2 correlation for the single-atom predictions of first ionization potential, melting point, and boiling point were 0.3589, 0.4599, and 0.1798 respectively. The corresponding Q2 correlations using orbital parameters describing two-atom systems increased the capability to predict the experimental properties to the respective values of 0.8551, 0.8207, and 0.7877. The accuracy in predicting materials’ bulk properties was increased up to four-fold by using two atoms instead of one. We also present results of the prediction of molecules for materials relevant to energy systems.