AI-Based Prediction of Electronic Properties of GaAs Materials

Abstract

The advancements in machine learning algorithms have considered all kinds of techniques that can help in analyzing the atomistic structure and properties of quantum confined nanostructures effectively. In this study, a machine learning algorithm based on regression fine tree is used for solving the current-voltage characteristics model for GaAs nanotube within the quantum confinement effect. The dimensions of nanotube are 3.52 nm in length and 3.61 nm in width. In this paper, the predictive distribution of the current-voltage characteristics models is estimated with the sufficient confidence level. This is not an easy task as there is a backscattering effect of the quantum confined nanostructure because of the mean-free path of the channel length. With such kind of quantum interference, there are challenges in predicting the current voltage characteristics. Hence, with the help of this machine learning algorithm, we have estimated this current-voltage characteristics model with negligible error rate.