PERFORMANCE COMPARISON OF ANFIS, ANN, SVR, CART AND MLR TECHNIQUES FOR GEOMETRY OPTIMIZATION OF CARBON NANOTUBES USING CASTEP

Year 2018, Volume: 2 Issue: 3, 119 - 124, 01.09.2018

Mehmet Acı , Çiğdem İnan Acı , Mutlu Avcı

https://doi.org/10.31127/tuje.408976

Cited By: 5

https://izlik.org/JA55MW97HP

Abstract

Density Functional Theory (DFT) calculations used in the Carbon Nanotubes (CNT) design take a very long time even in the simulation environment as it is well known in literature. In this study, calculation time of DFT for geometry optimization of CNT is reduced from days to minutes using seven artificial intelligence-based and one statistical-based methods and the results are compared. The best results are achieved from ANFIS and ANN based models and these models can be used instead of CNT simulation software with high accuracy. 

Keywords

Geometry Optimization , Cnt , Dft , Artificial Intelligence

References

• Abo-Elhadeed, A. F. (2012). "Modeling carbon nanotube transistors using neural networks approach." Proc., International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), pp. 125-128.
• Acı, M. and Avcı, M. (2016). "Artificial neural network approach for atomic coordinate prediction of carbon nanotubes." Applied Physics A, vol. 7 No. 122, pp. 1-14.
• Akbari, E., Buntat, Z., Enzevaee, A., Ebrahimi, M., Yazdavar, A. H. and Yusof, R. (2014). "Analytical modeling and simulation of I–V characteristics in carbon nanotube based gas sensors using ANN and SVR methods." Chemometrics and Intelligent Laboratory Systems, Vol. 137, No. 1, pp. 173-180.
• CASTEP Biovia Materials. http://accelrys.com/products/collaborative-science/biovia-materials-studio/quantom-catalysis-software.html [Accessed 10 May 2017].
• Cheng, W., Cai, C., Luo, Y., Li, Y. and Zhao, C. (2015). "Mechanical properties prediction for carbon nanotubes/epoxy composites by using support vector regression." Modern Physics Letters B, Vol. 29, No. 05, pp. 1550016.
• Eberly, L. E. (2007). Multiple linear regression. In: Topics in biostatistics, Humana Press, Totowa, NJ, USA.
• Ensafi, A. A., Taei, M., Khayamian, T. and Hasanpour, F. (2010). "Simultaneous voltammetric determination of enrofloxacin and ciprofloxacin in urine and plasma using multiwall carbon nanotubes modified glassy carbon electrode by least-squares support vector machines." Analytical Sciences, Vol. 26, No. 7, pp. 803-808.
• Gupta, N. (2013). "Artificial neural network." Network and Complex Systems, Vol. 3, No. 1, pp. 24-28.
• Hassanzadeh, Z., Kompany-Zareh, M., Ghavami, R., Gholami, S. and Malek-Khatabi, A. (2015). "Combining radial basis function neural network with genetic algorithm to QSPR modeling of adsorption on multi-walled carbon nanotubes surface." Journal of Molecular Structure, Vol. 1098, No. 1, pp. 191-198.
• Hayati, M., Rezaei, A. and Seifi, M. (2010). "CNT-MOSFET modeling based on artificial neural network: Application to simulation of nanoscale circuits." Solid-State Electronics, Vol. 54, No. 1, pp. 52-57.
• Jang, J.-S. (1993). "ANFIS: adaptive-network-based fuzzy inference system." IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685.
• Kanamitsu, K. and Saito, S. (2002). "Geometries, electronic properties, and energetics of isolated single walled carbon nanotubes." Journal of the Physical Society of Japan, Vol. 71, No. 2, pp. 483-486.
• Kohn, W. and Sham, L. J. (1965). "Self-consistent equations including exchange and correlation effects." Physical review, Vol. 140, No. 4A, pp. A1133–A1138.
• Kürti, J., Zólyomi, V., Kertesz, M. and Sun, G. (2003). "The geometry and the radial breathing mode of carbon nanotubes: beyond the ideal behaviour." New Journal of Physics, Vol. 5, No. 1, pp. 125.
• Lawrence, R. L. and Wright, A. (2001). "Rule-based classification systems using classification and regression tree (CART) analysis." Photogrammetric Engineering and Remote sensing, Vol. 67, No. 10, pp. 1137-1142.
• MATLAB. https://www.mathworks.com/ [Accessed 12 May 2017].
• Moradian, R., Behzad, S. and Chegel, R. (2008). "Ab initio density functional theory investigation of structural and electronic properties of silicon carbide nanotube bundles." Physica B: Condensed Matter, Vol. 403, No. 19, pp. 3623-3626.
• Moradian, R., Behzad, S. and Chegel, R. (2009). "Ab initio density functional theory investigation of Li-intercalated silicon carbide nanotube bundles." Physics Letters A, Vol. 373, No. 26, pp. 2260-2266.
• Moré J.J. (1978). The levenberg-marquardt algorithm: implementation and theory. In: Numerical analysis, Springer, Berlin, Germany.
• Rahimi-Nasrabadi, M., Akhoondi, R., Pourmortazavi, S. M. and Ahmadi, F. (2015). "Predicting adsorption of aromatic compounds by carbon nanotubes based on quantitative structure property relationship principles." Journal of Molecular Structure, Vol. 1099, No. 1, pp. 510-515.
• Salehi, E., Abdi, J. and Aliei, M. H. (2016). "Assessment of Cu (II) adsorption from water on modified membrane adsorbents using LS-SVM intelligent approach." Journal of Saudi Chemical Society, Vol. 20, No. 2, pp. 213-219.
• Shanbedi, M., Jafari, D., Amiri, A., Heris, S. Z. and Baniadam, M. (2013). "Prediction of temperature performance of a two-phase closed thermosyphon using artificial neural network." Heat and Mass Transfer, Vol. 49, No. 1, pp. 65-73.
• Smola, A. and Vapnik, V. (1997). "Support vector regression machines." Advances in Neural Information Processing Systems, Vol. 9, No. 1, pp. 155-161.
• Takagi, T. and Sugeno, M. (1985). "Fuzzy identification of systems and its applications to modeling and control." IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-15, No. 1, pp. 116-132.
• Witten, I. H. and Frank, E. (2005). Data mining: practical machine learning tools and techniques, Morgan Kaufmann, San Francisco, USA.
• Yagi, Y., Briere, T. M., Sluiter, M. H., Kumar, V., Farajian, A. A. and Kawazoe, Y. (2004). "Stable geometries and magnetic properties of single-walled carbon nanotubes doped with 3 d transition metals: A first-principles study." Physical Review B, Vol. 69, No. 7, pp. 075414.