Strain energy prediction of single wall carbon nanotubes using general regression neural network and adaptive neuro - Fuzzy Inference System
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Single Wall Carbon Nanotubes (SWCNTs) play crucial roles in the field of nanotechnology research and applications. Due to their quantum mechanical nature and the intricate structure of SWCNTs, performing direct or indirect experimental processes can be exceedingly challenging. Simulation methods like Tight-Binding Molecular Dynamics (TBMD) can serve as viable alternatives to experimentation. However, it's worth noting that these methods often demand extensive computational runtime. To address this computational time challenge, artificial intelligence algorithms such as the General Regression Neural Network (GRNN) and the Adaptive Neuro Fuzzy Interface System (ANFIS) have been proposed in this study. These models aim to calculate the energetic properties of SWCNTs more efficiently, offering practical and quicker predictive methods with reduced computational workloads. The study's findings demonstrate a strong correlation between the predicted energy values of SWCNTs using GRNN and ANFIS models and the results obtained through TBMD simulations. Consequently, it is believed that these models can be suitable and effective approaches for computing the energetic properties of SWCNTs.
Açıklama
Anahtar Kelimeler
Artificial Neural Networks, Carbon Nanotubes, Computer Simulation, Electronic-Structure, Simulation, Diffusion
Kaynak
Electric Power Components and Systems
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
Sayı
Künye
Eyecioglu, O., Kayisli, K., & Beken, M. (2024). Strain Energy Prediction of Single Wall Carbon Nanotubes Using General Regression Neural Network and Adaptive Neuro–Fuzzy Inference System. Electric Power Components and Systems, 52(8), 1437-1447.
