The statistical neural network-based regression approach for prediction of optical band gap of CuO

The design of CuO nanostructured semiconductors and the engineering of its optical band gaps have become important ways to further improve the performance of functional nano-devices such as energy conversion applications and optoelectronics. The optical band gap ( E g ) of a semiconductor is a cruci...

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Bibliographic Details
Published in:Indian journal of physics 2022, Vol.96 (12), p.3547-3557
Main Authors: Ruzgar, Serif, Acar, Emrullah
Format: Article
Language:English
Subjects:
Astrophysics and Astroparticles
Energy bands
Energy conversion
Energy gap
Machine learning
Nanotechnology devices
Neural networks
Optoelectronic devices
Original Paper
Parameters
Performance enhancement
Photovoltaic cells
Physics
Physics and Astronomy
Regression models
Semiconductor materials
Solar cells
Statistical analysis
Statistical prediction
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Summary:The design of CuO nanostructured semiconductors and the engineering of its optical band gaps have become important ways to further improve the performance of functional nano-devices such as energy conversion applications and optoelectronics. The optical band gap ( E g ) of a semiconductor is a crucial parameter that defines its performance in solar cell applications. Therefore, determining the E g of the semiconductor and comprehensively examining the relationship between the E g and the structure of the material will be a guide to improve the performance of optoelectronic device applications. However, the relationship between E g and structural properties of CuO is complex, and the combinations of variation in the E g of CuO with various production parameters and doping materials are tremendous. For this reason, employing machine learning techniques can be a cheap, easy and effective approach to predict the E g value of CuO. In this study, a statistical neural network (SNN)-based regression model is proposed to predict the energy band gap for the CuO semiconductor. A total of 100 CuO materials with optical band gaps between 1.02 and 3.41 eV are examined. With the proposed SNN approach, the optical band gap of CuO is predicted with low mean errors in terms of RMSE and MAE thanks to employing the structural parameters of the semiconductor material.
ISSN:0973-1458
0974-9845
DOI:10.1007/s12648-022-02283-6