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In this work, we propose an efficient computational scheme for first-principle quantum transport simulations to evaluate the open-boundary conditions. Its partitioning differentiates from conventional methods in that the contact self-energy matrices are constructed on smaller building blocks, principal layers (PL), while conventionally it was restricted to have the same lateral dimensions of the adjoining atoms in a channel region. Here, we obtain the properties of bulk electrodes through non-equilibrium Greens function (NEGF) approach with significant improvements in the computational efficiency without sacrificing the accuracy of results. To exemplify the merits of the proposed method we investigate the carrier density dependency of contact resistances in silicon nanowire devices connected to bulk metallic contacts.
A theoretical study of the surface energy-loss function of freestanding Pb(111) thin films is presented, starting from the single monolayer case. The calculations are carried applying the linear response theory, with inclusion of the electron band st
We present a novel ab initio non-equilibrium approach to calculate the current across a molecular junction. The method rests on a wave function based description of the central region of the junction combined with a tight binding approximation for th
We present a novel ab initio non-equilibrium approach to calculate the current across a molecular junction. The method rests on a wave function based full ab initio description of the central region of the junction combined with a tight binding appro
This lecture note reviews recently proposed sparse-modeling approaches for efficient ab initio many-body calculations based on the data compression of Greens functions. The sparse-modeling techniques are based on a compact orthogonal basis representa
We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning. It is assumed that the atomic energy is represented by the linear regression