Atomic-Scale Simulations of Si-Ge-Sn Alloys Using Deep-Learning-Based Interatomic Potentials

Accurate knowledge and understanding of thermal conductivity is very important in awide variety of applications both at microscopic and macroscopic scales. Estimation,however varies widely with respect to scale and application. At a lattice level, calcu-lation of thermal conductivity of any particular alloy require very heavy computationeven for a relatively small number of atoms. This thesis aims to run conventionalmolecular dynamic simulations for a particular supercell and then employ a machinelearning based approach and compare the two in hopes of developing a method togreatly reduce computational costs as well as increase the scale and time frame ofthese systems. Conventional simulations were run using interatomic potentials basedon density function theory-basedab initiocalculations. Then deep learning neuralnetwork based interatomic potentials were used run similar simulations to comparethe two approaches.