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