Accurate prediction of bonding properties by a machine learning-based model using isolated states before bonding

摘要

Bonding characters, such as length and strength, are of key importance for material structure and properties. Here, a machine learning (ML) model is used to predict the 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 the 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 prediction of bonding and adsorption properties.