General purpose Slater-Koster tight-binding code for electronic structure calculations

Overview

tight-binder

Introduction

General purpose tight-binding code for electronic structure calculations based on the Slater-Koster approximation. The code is yet to be finished: so far the modules include the strictly necessary routines to compute band structures without additional information. It is designed to allow band structure calculations of alloys up to two atomic species (provided one gives the corresponding SK amplitudes).

The idea behind the program is to allow calculations simply using the configuration file, without any need to fiddle with the code (although that option is always available). Some examples are provided (cube.txt, chain.txt) which show the parameters needed to run a simulation.

  • Last Update: Added spin-orbit coupling up to d orbitals

Installation

Usage of a virtual environment is recommended to avoid conflicts, specially since this package is still in development so it will experiment changes periodically.

  • From within the root folder of the repository, install the required packages:
$ cd {path}/tightbinder
$ pip install -r requirements.txt
  • Then install the tightbinder package
$ pip install .
  • You can use the application from within the repository, using the bin/app.py program in the following fashion:
$ python bin/app.py {config_file} 

Or since the library is installed, create your own scripts. For now, usage of the app.py program is advised.

Documentation

To generate the documentation, you must have installed GNU Make previously. To do so, simply $ cd docs/source and run $ make html. The documentation will then be created in docs/build/html.

Examples

The folder examples/ contains some basic cases to test that the program is working correcly.

  • One-dimensional chain (1 orbital): To run the example do $ python bin/app.py examples/chain.txt

This model is analytically solvable, its band dispersion relation is:

alt text

  • Bi(111) bilayer: To run it: $python bin/app.py examples/bi(111).txt In this case we use a four-orbital model (s, px, py and pz). Since we are modelling a real material, we need to input some valid Slater-Koster coefficients as well as the spin-orbit coupling amplitude. These are given in [1, 2].

The resulting band structure is:

alt text

Bi(111) bilayers are known to be topological insulators. To confirm this, one can use the routines provided in the topology module to calculate its invariant.

To do so, we can compute its hybrid Wannier centre flow, which results to be:

alt text

The crossing of the red dots indicates that the material is topological. For more complex cases, there is a routine implemented to automatize the counting of crossings, based on [3].

Workroad

The future updates will be:

  • hamiltonian.py: Module for inititializing and solving the Hamiltonian of the system given in the config. file
  • topology.py: This module will include routines for computing topological invariants of the system. (19/12/20) Z2 invariant routines added. It remains to fix routines related to Chern invariant.
  • disorder.py: Module with routines to introduce disorder in the system such as vacancies or impurities

A working GUI might be done in the future

References

Owner
PhD student in Physics
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis

Readme File for "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" by Ham, Imai, and Janson. (2022) All scripts were written and

0 Jan 27, 2022
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021) PyTorch implementation of SnapMix | paper Method Overview Cite

DavidHuang 126 Dec 30, 2022
Official repository of DeMFI (arXiv.)

DeMFI This is the official repository of DeMFI (Deep Joint Deblurring and Multi-Frame Interpolation). [ArXiv_ver.] Coming Soon. Reference Jihyong Oh a

Jihyong Oh 56 Dec 14, 2022
Code repository for the paper: Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild (ICCV 2021)

Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild Akash Sengupta, Ignas Budvytis, Robert

Akash Sengupta 149 Dec 14, 2022
Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022)

Blockwise Sequential Model Learning Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022) For ins

2 Jun 17, 2022
This provides the R code and data to replicate results in "The USS Trustee’s risky strategy"

USSBriefs2021 This provides the R code and data to replicate results in "The USS Trustee’s risky strategy" by Neil M Davies, Jackie Grant and Chin Yan

1 Oct 30, 2021
Lingvo is a framework for building neural networks in Tensorflow, particularly sequence models.

Lingvo is a framework for building neural networks in Tensorflow, particularly sequence models.

2.7k Jan 05, 2023
Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC)

ppg-vc Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC) This repo implements different kinds of PPG-based VC models. Pretrained models. More m

Liu Songxiang 227 Dec 28, 2022
ViSD4SA, a Vietnamese Span Detection for Aspect-based sentiment analysis dataset

UIT-ViSD4SA PACLIC 35 General Introduction This repository contains the data of the paper: Span Detection for Vietnamese Aspect-Based Sentiment Analys

Nguyễn Thị Thanh Kim 5 Nov 13, 2022
The code for two papers: Feedback Transformer and Expire-Span.

transformer-sequential This repo contains the code for two papers: Feedback Transformer Expire-Span The training code is structured for long sequentia

Facebook Research 125 Dec 25, 2022
Code for Paper: Self-supervised Learning of Motion Capture

Self-supervised Learning of Motion Capture This is code for the paper: Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki, Self-sup

Hsiao-Yu Fish Tung 87 Jul 25, 2022
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation. (CVPR 2021)

GDR-Net This repo provides the PyTorch implementation of the work: Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji. GDR-Net: Geometry-Guided

169 Jan 07, 2023
A very simple baseline to estimate 2D & 3D SMPL-compatible keypoints from a single color image.

Minimal Body A very simple baseline to estimate 2D & 3D SMPL-compatible keypoints from a single color image. The model file is only 51.2 MB and runs a

Yuxiao Zhou 49 Dec 05, 2022
blind SQLIpy sebuah alat injeksi sql yang menggunakan waktu sql untuk mendapatkan sebuah server database.

blind SQLIpy Alat blind SQLIpy ini merupakan alat injeksi sql yang menggunakan metode time based blind sql injection metode tersebut membutuhkan waktu

Galih Anggoro Prasetya 4 Feb 24, 2022
A universal framework for learning timestamp-level representations of time series

TS2Vec This repository contains the official implementation for the paper Learning Timestamp-Level Representations for Time Series with Hierarchical C

Zhihan Yue 284 Dec 30, 2022
Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ)

Real2CAD-3DV Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ) Group Member: Yue Pan, Yuanwen Yue, Bingxin Ke, Yujie He

24 Jun 22, 2022
RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020) Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng [PDF] [Supplementary M

Hong Wang 6 Sep 27, 2022
[ICCV'21] Neural Radiance Flow for 4D View Synthesis and Video Processing

NeRFlow [ICCV'21] Neural Radiance Flow for 4D View Synthesis and Video Processing Datasets The pouring dataset used for experiments can be download he

44 Dec 20, 2022