![]() ![]() Following this, new research concepts and technologies to promote the research and development of energy materials becomes necessary. The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low success probabilities, high time consumption, and high computational cost associated with the traditional methods of developing energy materials. Finally, using simulation models and machine learning would help reduce cost and experimentation time studying these properties. An understanding of the vibratory behavior in these nanomaterials would be helpful in the development of nanosensors. There is conclusive evidence that there is a relationship between detecting defects in carbon nanotubes and the number of iterations required to describe thermionic and vibrational properties using machine learning. Mechanical properties present an open line of research regarding vibrational properties, where chiral geometric parameters are used to study the vibrational response of carbon nanotubes therefore, more accurate estimates are required to predict these frequencies. Regarding the thermal, electrical, and electronic properties, it is still necessary to complement the molecular dynamics and density functional theory results, respectively, with machine learning. The evaluation of mechanical, thermal, electrical, and electronic properties of carbon nanotubes has been reported. The importance of artificial neural networks, support vector machines, decision trees, random forests, and K-Nearest Neighbors is highlighted, mainly in analyzing these nanostructures. This work reveals a remarkable match between the amount of experimental data, the number of parameters, and the algorithms used to model uncontrolled physical properties exhibited by carbon nanotubes. In this review, we analyze the machine learning approaches used in modeling physical properties in carbon nanotubes. Carbon nanotubes have been studied to describe their properties or predict possible material responses during their synthesis process or in different conditions and environments. Machine learning has proven to be technically flexible in recent years, which allows it to be successfully implemented in problems in various areas of knowledge. This shows the power of the machine learning approach, which gives the results consistent with the density functional theory’s results. The absolute (relative) errors between the predicted by machine learning algorithm and density functional theory results of electron–phonon coupling constants are 0.01 (2%) and 0.13 (15%) for uranium and lutetium, respectively. Then, we constructed a model by machine learning approach for 28 superconducting elements of the periodic table and chose the Debye and superconducting transition temperatures as descriptors features, to predict the electron–phonon coupling constants for uranium and lutetium. The electronic density of states peak that is around the Fermi energy and the sharp peaks in phonon dispersion curve are larger in magnitude for uranium, so the electron–phonon interaction in uranium is higher than that in lutetium. These values were calculated using density functional perturbation theory for uranium and lutetium, which are not available in the literatures. According to the importance of electron–phonon interaction, we present an ab initio study of the electron–phonon coupling constant of two superconducting elements of periodic table. ![]()
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