Abstract

Graphene is a 2D material obtained from exfoliation of Graphite and possesses exotic electrical and thermal properties in the form of high electrical conductivity, high lattice thermal conductivity and several folds anisotropy between in-plane and cross-plane lattice thermal conductivities. Studying these transport properties requires high throughput first principles calculations and computationally intensive simulations which enable predictive design of materials for applications like energy conversion, heat dissipation in electronic systems and sensors. The following work aims to predict the lattice thermal conductivity of Monolayer Graphene (MLG) by the direct solution of linearized phonon-Boltzmann Transport Equation (LBTE) using phono3py code, integrated with first principles calculations implemented in Quantum ESPRESSO. All first principle calculations are accelerated on an NVIDIA GeForce RTX 4090 GPU and BTE computation is parallelized using an Intel Core i9-13900K processor. Hardware benchmarking of scf calculation of a single displacement file for various wave-vector grid sizes is performed. GPU proves to be a faster computational hardware with computing time ≈2 orders of magnitude lower than that of the CPU.