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首页> 外文期刊>Journal of computational electronics >Predicting model of Ⅰ-Ⅴ characteristics of quantum-confined GaAs nanotube: a machine learning and DFT-based combined framework
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Predicting model of Ⅰ-Ⅴ characteristics of quantum-confined GaAs nanotube: a machine learning and DFT-based combined framework

机译：Predicting model of Ⅰ-Ⅴ characteristics of quantum-confined GaAs nanotube: a machine learning and DFT-based combined framework

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摘要

Continuous developments of machine learning algorithms have covered the various ways to analyze the atomistic structure and characteristics of quantum-confined nanostructures effectively. This work presents a machine learning model based on a regression fine tree algorithm to resolve the current-voltage characteristics model for GaAs nanotube during quantum confinement. The nanotube is 3.52 nm long and 3.61 nm wide. This paper presents predictive distributions of the current-voltage characteristic model with a sufficiently high level of confidence. This is a challenging task due to the backscattering effect of the quantum-confined nanostructures while the channel length is beyond the mean free path. Due to this quantum interference, it is difficult to predict the current-voltage characteristics correctly for quantum-confined nanostructures. Therefore, this machine learning approach helps to predict the model almost accurately with negligible erroneous values. This framework introduces a combined approach for both DFT and machine learning algorithms with lesser time cost and high predictivity response.

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