EFFECT OF AMORPHOUS LEVELS ON NANOWIRE CONDUCTIVITY AND PREDICTION USING MACHINE LEARNING

• Main Author: Jemmi Ariesta, Jeriko
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/74604
• Institution: Institut Teknologi Bandung

Nanowires are cylindrical structures with a cross section of nanometers and lengths in the micrometer range. Because of their small size, nanowires could be used as a component in nanoscale devices. Electrical conductivity, or the ability of a material to conduct electric current, is an important property of nanowires. Many researchers have investigated the theoretical and experimental conductivity properties of nanowires, including size effects, doping effects, surface effects, and so on. However, no research has been conducted on the effect of amorphous levels on the conductivity of nanowires. The amorphous degree here is a measure of the random shift in the position of the atoms relative to an ideal lattice point. We investigate the effect of randomized atomic positions on electrical conductivity in this final project. This study is based on the assumption that as the size of the material decreases (on the nanometer or angstrom scale), the position of the atoms deviates from the ideal lattice point, resulting in an amorphous structure. The research was carried out by simulating with Quantum ESPRESSO software that already had BoltzTraP installed. BURAI and other software are used to create a model of the nanowire structure to be simulated. The parameter to be investigated in this study is conductivity, which can be calculated if the Fermi energy is known. The Fermi energy can be calculated using Quantum ESPRESSO. The conductivity will then be calculated by BoltzTraP using the Boltzmann Transport Equation. Machine learning will be used to make predictions based on the conductivity data obtained.