Predicting Model for Device Density of States of Quantum-Confined SiC Nanotube with Magnetic Dopant: An Integrated Approach Utilizing Machine Learning and Density Functional Theory

We investigate the influence of spin and impurity on the density of states of SiC nanotubes employing Density Functional Theory (DFT) and a Machine Learning (ML) based framework. Our study investigates the electronic structures and magnetic properties of various SiC nanotube configurations, including wurtzite, Co-doped, and undoped single-wall (6,0) chiral nanotubes, employing both DFT and pseudopotential approaches. The calculated energy band gap values for SiC bulk structures, nanotubes, and doped systems, retaining local density and local spin density approximations with the Hubbard U method, exhibit distinct characteristics. While undoped SiC systems remain nonmagnetic whereas Co-doped SiC systems show magnetic properties, with a total magnetic moment of around ~ 1.9 µB. Our first-principles calculations indicate that Co-doped SiC nanotubes induce magnetism, however the total energy calculations revealed satisfactory stability for the ferromagnetic phase. Validation against DFT data demonstrates that our model achieves approximately 91.5% accuracy for predicting the density of states for quantum-confined SiC nanotube structures and also showcasing significant potential for further electronic properties calculations in this domain. Integrating ML algorithms with DFT-based approach presents an efficient algorithm for predicting total density of states in quantum-confined nanoscale structures. The fine tree regression algorithm shows highly efficient and effective prediction for density of states calculations.