Perovskite tetragonality modeling for functional properties enhancement using Newtonian search based support vector regression computational method

Authors

  • Peter Chibuike Okoye Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, 342111, Ondo State
  • Samuel Ogochukwu Azi Department of Physics, University of Benin, Benin City, Edo State, Nigeria.
  • Taoreed O. Owolabi Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko, 342111, Ondo State, Nigeria

Keywords:

Support vector regression; tetragonality; distortion; perovskite; Newtonian based gravitational search algorithm.

Abstract

Tetragonality occurs as a result of stretching the crystal structural lattice of perovskite along one of its lattice vectors such that the three axes are mutually perpendicular with two of the axes having equal lengths. This tetragonality distortion easily triggers functional properties such as pyroelectricity, ferroelectricity, capacitance and piezoelectricity among others, while synthesizing functional ceramics for a particular application. This work addresses and circumvents the challenges of experimental stress involved in functional ceramics synthesis by developing Newtonian search based support vector regression (GSB-SVR) model for perovskite tetragonality prediction using dopants concentration and ionic radii as the model predictors. The performance of proposed GSB-SVR model is compared with the existing method and better performance is obtained. The influence of lanthanides and zirconium incorporation on functional ceramics on the material tetragonality is also modeled by the developed GSB-SVR model. The precision of the developed model, its easily fetched predictors  and pre-laboratory ability to effectively and efficiently model the perovskite tetragonality are of high importance in tailoring and enhancing functional properties of materials for desired applications.

Dimensions

K. R. Tolman & R. Ubic, “An empirical model for perovskite tetragonality”, J. Alloys Compd. 690 (2017) 825, doi: 10.1016/j.jallcom.2016.08.182.

T. O. Owolabi, “Extreme learning machine and swarm-based support vector regression methods for predicting crystal lattice parameters of pseudo-cubic/cubic perovskites”, J. Appl. Phys. 127 (2020) 245107, doi: 10.1063/5.0008809.

L. Chonghe, T. Yihao, Z. Yingzhi, W. Chunmei, & W. Ping, “Prediction of lattice constant in perovskites of GdFeO3 structure”, J. Phys. Chem. Solids. 64 (2003) 2147, doi:10.1016/S0022- 3697(03)00209-9.

A. Majid, A. Khan, G. Javed, & A. M. Mirza, “Lattice constant prediction of cubic and monoclinic perovskites using neural networks and support vector regression”, Comput. Mater. 50 (2010) 363, doi: 10.1016/j.commatsci.2010.08.028.

R. Ubic & G. Subodh, “The prediction of lattice constants in orthorhombic perovskites”, J. Alloys Compd. 488 (2009) 374, doi: 10.1016/j.jallcom.2009.08.139.

R. Ubic, K. Tolman, K. Talley, B. Joshi, J. Schmidt, E. Faulkner, J. Owens, M. Papac, A. Garland, C. Rumrill, K. Chan, N. Lundy, H. Kungl, “Lattice-constant prediction & effect of vacancies in aliovalently doped perovskites”, J. Alloys Compd. 644 (2015) 982, doi:10.1016/j.jallcom.2015.04.213.

M. Ganguly, S. K. Rout, T. P. Sinha, S. K. Sharma, H. Y. Park, C. W. Ahn, I. W. Kim, “Characterization and Rietveld Refinement of A-site deficient Lanthanum doped Barium Titanate”, J. Alloys Compd. 579 (2013) 473, doi: 10.1016/j.jallcom.2013.06.104.

Vapnik V., “The Nature of statistical Learning Theory”, Springer, New York. (1995) 30, ISBN: 964-7445-88-1.

T. O. Owolabi, “Determination of the Velocity of Detonation of Primary Explosives Using Genetically Optimized Support Vector Regression”, Propellants, Explos. Pyrotech. 44 (2019) 1282, doi: 10.1002/prep.201900077.

E. Rashedi, H. Nezamabadi-pour, & S. Saryazdi, “GSA: A Gravitational Search Algorithm”, Inf. Sci. (Ny) 179 (2009) 2232, doi: 10.1016/j.ins.2009.03.004.

D. Ezzat, A. E. Hassanien, & H. A. Ella, “An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization”, Appl. Soft Comput. (2020) 106742, doi: 10.1016/j.asoc.2020.106742.

M. Magdy, A. El Marhomy, & M. A. Attia, “Modeling of inverted pendulum system with gravitational search algorithm optimized controller”, Ain Shams Eng. 10 (2019) 129, doi:10.1016/j.asej.2018.11.001.

J. Jiang, X. Yang, X. Meng, & K. Li, “Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems”, Phys. A Stat. Mech. its Appl. 537 (2020) 122621, doi:10.1016/j.physa.2019.122621.

Q. S. Banyhussan, A. N. Hanoon, A. Al-Dahawi, G. Yildirim, & A. A. Abdulhameed, “Development of gravitational search algorithm model for predicting packing density of cementitious pastes”, J. Build. Eng. 27 (2020) 100946, doi: 10.1016/j.jobe.2019.100946.

S. Duman, U. Guvenc, Y. Sonmez, & N. Yorukeren, “Optimal power flow using gravitational search algorithm”, Energy Convers. Manag. 59 (2012) 86, doi:10.1016/j.enconman.2012.02.024.

V. N. Vapnik, “An overview of statistical learning theory”, IEEE Trans. Neural Networks. 10 (1999) 988, doi: 10.1109/72.788640.

A. A. Adewumi, T. O. Owolabi, I. O. Alade, & S. O. Olatunji, “Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach”, Appl. Soft Comput. J. 42 (2016) 342, doi: 10.1016/j.asoc.2016.02.009.

T. A. Oyehan, I. O. Alade, A. Bagudu, K. O. Sulaiman, S. O. Olatunji, & T. A. Saleh, “Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach”, Comput. Biol. Med. 98 (2018) 85, doi: 10.1016/j.compbiomed.2018.04.024.

M. Ghorbani, G. Zargar, & H. Jazayeri-Rad, “Prediction of asphaltene precipitation using support vector regression tuned with genetic algorithms”, Petroleum 2 (2016) 301, doi:10.1016/j.petlm.2016.05.006.

A. Mariette & K. Rahul, “Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers”, Apress. (2015) 70, doi: 10.1007/978-1-4302-5990-9.

Published

2022-02-28

How to Cite

Perovskite tetragonality modeling for functional properties enhancement using Newtonian search based support vector regression computational method. (2022). Journal of the Nigerian Society of Physical Sciences, 4(1), 20-26. https://doi.org/10.46481/jnsps.2022.248

Issue

Volume 4, Issue 1, February 2022

Section

Special Issue "Machine Learning Computational Methods"
Copyright & Licensing

Copyright (c) 2022 Journal of the Nigerian Society of Physical Sciences

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Perovskite tetragonality modeling for functional properties enhancement using Newtonian search based support vector regression computational method. (2022). Journal of the Nigerian Society of Physical Sciences, 4(1), 20-26. https://doi.org/10.46481/jnsps.2022.248