Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm-Based Support Vector Regression

Author:

Okoye Peter Chibuike12,Azi Samuel Ogochukwu2,Owolabi Taoreed O.1ORCID,Adeyemi Oke Wasiu3,Souiyah Miloud4,Latif Mouftahou B.56,Olusayo Olubosede7

Affiliation:

1. Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, 342111 Ondo State, Nigeria

2. Department of Physics, University of Benin, Benin City, Edo State, Nigeria

3. Department of Mechanical and Mechatronics Engineering, Afe Babalola University Ado-Ekiti, P.M.B 5454, Ado Ekiti, Nigeria

4. Department of Mechanical Engineering, College of Engineering, University of Hafr Al Batin, P.O. Box 1803, Hafr Al Batin 31991, Saudi Arabia

5. Centre for Energy Research and Development, Obafemi Awolowo University, Ile-Ife 220005, Nigeria

6. KU Leuven, Instituut voor Kern- en Stralingsfysica, B-3001 Leuven, Belgium

7. Physics Department, Federal University Oye Ekiti, Oye Ekiti, Ekiti State, Nigeria

Abstract

Tin (II) sulfide (SnS) is a metal chalcogenide semiconducting material with fascinating and admirable physical features for practical applications in solid-state batteries, photodetectors, gas sensors, optoelectronic devices, emission transistors, and photocatalysis among others. The energy gap of SnS semiconductor nanomaterial that facilitates its usefulness in many applications can be adjusted through dopant incorporation which results in crystal lattice distortion at various crystallite sizes of the semiconductor. This work employs lattice parameter descriptors to develop a hybrid genetic algorithm (GA) and support vector regression algorithm (SVR) intelligent model for determining the energy gap of doped SnS semiconductors. The predictive strength of the developed GA-SVR model is compared with the stepwise regression algorithm- (STRA-) based model using different performance evaluation parameters. The developed GA-SVR model performs better than STRA model based on root mean square error, mean absolute error, and correlation coefficient with performance improvement of 70.68%, 67.63%, and 20.98%, respectively, using the testing set of data. Influence of different dopants and experimental conditions on energy gap of SnS semiconductor were investigated using the developed model, while the obtained values for the energy gaps agree with the measured values. The developed models demonstrate high degree of potentials in terms of accuracy, precision, and ease of implementation that fosters their real-life applicability in estimating the energy gap of doped SnS semiconductor with experimental stress circumvention.

Funder

Tertiary Education Trust Fund

Publisher

Hindawi Limited

Subject

General Materials Science

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