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Register a new userNanoNET: an extendable Python framework for semi-empirical tight-binding models
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Added by
Mykhailo Klymenko Dr
Publication date
2020
fields
Physics
and research's language is
English
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We present a novel open-source Python framework called NanoNET (Nanoscale Non-equilibrium Electron Transport) for modelling electronic structure and transport. Our method is based on the tight-binding method and non-equilibrium Greens function theory. The core functionality of the framework is providing facilities for efficient construction of tight-binding Hamiltonian matrices from a list of atomic coordinates and a lookup table of the two-center integrals in dense, sparse, or block-tridiagonal forms. The framework implements a method based on $kd$-tree nearest-neighbour search and is applicable to isolated atomic clusters and periodic structures. A set of subroutines for detecting the block-tridiagonal structure of a Hamiltonian matrix and splitting it into series of diagonal and off-diagonal blocks is based on a new greedy algorithm with recursion. Additionally the developed software is equipped with a set of programs for computing complex band structure, self-energies of elastic scattering processes, and Greens functions. Examples of usage and capabilities of the computational framework are illustrated by computing the band structure and transport properties of a silicon nanowire as well as the band structure of bulk bismuth.
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For a previously published study of the titanium hcp (alpha) to omega (omega) transformation, a tight-binding model was developed for titanium that accurately reproduces the structural energies and electron eigenvalues from all-electron density-functional calculations. We use a fitting method that matches the correctly symmetrized wavefuctions of the tight-binding model to those of the density-functional calculations at high symmetry points. The structural energies, elastic constants, phonon spectra, and point-defect energies predicted by our tight-binding model agree with density-functional calculations and experiment. In addition, a modification to the functional form is implemented to overcome the collapse problem of tight-binding, necessary for phase transformation studies and molecular dynamics simulations. The accuracy, transferability and efficiency of the model makes it particularly well suited to understanding structural transformations in titanium.
We consider atomistic geometry relaxation in the context of linear tight binding models for point defects. A limiting model as Fermi-temperature is sent to zero is formulated, and an exponential rate of convergence for the nuclei configuration is established. We also formulate the thermodynamic limit model at zero Fermi-temperature, extending the results of [H. Chen, J. Lu, C. Ortner. Arch. Ration. Mech. Anal., 2018]. We discuss the non-trivial relationship between taking zero temperature and thermodynamic limits in the finite Fermi-temperature models.
Atomistic models like tight-binding (TB), bond-order potentials (BOP) and classical potentials describe the interatomic interaction in terms of mathematical functions with parameters that need to be adjusted for a particular material. The procedures for constructing TB/BOP models differ from the ones for classical potentials. We developed the BOPcat software package as a modular python code for the construction and testing of TB/BOP parameterizations. It makes use of atomic energies, forces and stresses obtained by TB/BOP calculations with the BOPfox software package. It provides a graphical user interface and flexible control of raw reference data, of derived reference data like defect energies, of automated construction and testing protocols, and of parallel execution in queuing systems. We outline the concepts and usage of the BOPcat software and illustrate its key capabilities by exemplary constructing and testing of an analytic BOP for Fe. The parameterization protocol with a successively increasing set of reference data leads to a magnetic BOP that is transferable to a variety of properties of the ferromagnetic bcc groundstate and to crystal structures that were not part of the training set.
Computational physics problems often have a common set of aspects to them that any particular numerical code will have to address. Because these aspects are common to many problems, having a framework already designed and ready to use will not only speed the development of new codes, but also enhance compatibility between codes. Some of the most common aspects of computational physics problems are: a grid, a clock which tracks the flow of the simulation, and a set of models describing the dynamics of various quantities on the grid. Having a framework that could deal with these basic aspects of the simulation in a common way could provide great value to computational scientists by solving various numerical and class design issues that routinely arise. This paper describes the newly developed computational framework that we have built for rapidly prototyping new physics codes. This framework, called turboPy, is a lightweight physics modeling framework based on the design of the particle-in-cell code turboWAVE. It implements a class (called Simulation) which drives the simulation and manages communication between physics modules, a class (called PhysicsModule) which handles the details of the dynamics of the various parts of the problem, and some additional classes such as a Grid class and a Diagnostic class to handle various ancillary issues that commonly arise.
We introduce a Python framework designed to automate the most common tasks associated with the extraction and upscaling of the statistics of single-impact crater functions to inform coefficients of continuum equations describing surface morphology evolution. Designed with ease-of-use in mind, the framework allows users to extract meaningful statistical estimates with very short Python programs. Wrappers to interface with specific simulation packages, routines for statistical extraction of output, and fitting and differentiation libraries are all hidden behind simple, high-level user-facing functions. In addition, the framework is extensible, allowing advanced users to specify the collection of specialized statistics or the creation of customized plots. The framework is hosted on the BitBucket service under an open-source license, with the aim of helping non-specialists easily extract preliminary estimates of relevant crater function results associated with a particular experimental system.
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