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Given the strong dependence of material structure and properties on thelength and strength of constituent bonds and the fact that surface adsorptionand chemical reactions are initiated by the formation of bonds between twosystems, bonding parameters are of key importance for material design andindustrial processes. In this study, a machine learning (ML)-based model isused to accurately predict bonding properties from information pertaining toisolated systems before bonding. This model employs the density of states (DOS)before bond formation as the ML descriptor and accurately predicts bindingenergy, bond distance, covalent electron amount, and Fermi energy even whenonly 20% of the whole dataset is used for training. The results show that theDOS of isolated systems before bonding is a powerful descriptor for theaccurate prediction of bonding and adsorption properties.

Accurate Prediction of Bonding Properties by A Machine Learning-based Model using Isolated States Before Bonding