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Register a new userEfficient partitioning of surface Greens function: toward ab initio contact resistance study
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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.
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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 structure by means of a first-principles pseudopotential approach using a supercell scheme. Quantum-size effects on the plasmon modes of the thinnest films are found in qualitative agreement with previous work based on the jellium model. For thicker films, results show a dispersionless mode at all thicknesses, in agreeement with electron energy-loss measurements. For sizeable values of the momentum, the raising of the surface plasmon with increasing thickness is retrieved.
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 the electrodes in the frame of the Keldysh Greens function formalism. In addition we present an extension so as to include effects of the two-particle propagator. Our procedure is demonstrated for a dithiolbenzene molecule between silver electrodes. The full current-voltage characteristic is calculated. Specific conclusions for the contribution of correlation and two-particle effects are derived. The latter are found to contribute about 5% to the current. The order of magnitude of the current coincides with experiments.
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 approximation for the electrodes in the frame of the Keldysh Greens function formalism. Our procedure is demonstrated for a dithiolethine molecule between silver electrodes. The main conducting channel is identified and the full current-voltage characteristic is calculated.
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 representation, intermediate representation (IR) basis functions, for imaginary-time and Matsubara Greens functions. A sparse sampling method based on the IR basis enables solving diagrammatic equations efficiently. We describe the basic properties of the IR basis, the sparse sampling method and its applications to ab initio calculations based on the GW approximation and the Migdal-Eliashberg theory. We also describe a numerical library for the IR basis and the sparse sampling method, irbasis, and provide its sample codes. This lecture note follows the Japanese review article [H. Shinaoka et al., Solid State Physics 56(6), 301 (2021)].
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 of the atomic structural descriptor. The atomic energy is obtained through DFT calculations using a small cell extracted from a huge GB model, called replica DFT atomic energy. The uncertainty reduction (UR) approach in active learning is used to efficiently collect the training data for the atomic energy. In this approach, atomic energy is not required to search for candidate points; therefore, sequential DFT calculations are not required. This approach is suitable for massively parallel computers that can execute a large number of jobs simultaneously. In this study, we demonstrate the prediction of the atomic energy of a Fe random GB model containing one million atoms using the UR approach and show that the prediction error decreases more rapidly compared with random sampling. We conclude that the UR approach with replica DFT atomic energy is useful for modeling huge GBs and will be essential for modeling other structural defects.
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