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

TL;DR

This paper introduces a machine learning model that predicts bonding properties using the density of states of isolated systems, enabling accurate predictions with limited training data, which is valuable for material design and industrial applications.

Contribution

The study demonstrates that the density of states before bonding is an effective descriptor for predicting bonding properties with high accuracy using ML.

Findings

Accurately predicts binding energy, bond distance, covalent electron amount, and Fermi energy.

Achieves high prediction accuracy with only 20% of the dataset used for training.

Validates the effectiveness of DOS as a descriptor for bonding property prediction.

Abstract

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.