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
The code used for the free [email protected] Webinar series on Reinforcement Learning in Finance

Reinforcement Learning in Finance [email protected] Webinar This repository provides the code f

Yves Hilpisch 62 Dec 22, 2022
Waymo motion prediction challenge 2021: 3rd place solution

Waymo motion prediction challenge 2021: 3rd place solution 📜 Technical report 🗨️ Presentation 🎉 Announcement 🛆Motion Prediction Channel Website 🛆

158 Jan 08, 2023
Unofficial implement with paper SpeakerGAN: Speaker identification with conditional generative adversarial network

Introduction This repository is about paper SpeakerGAN , and is unofficially implemented by Mingming Huang ( 7 Jan 03, 2023

Employs neural networks to classify images into four categories: ship, automobile, dog or frog

Neural Net Image Classifier Employs neural networks to classify images into four categories: ship, automobile, dog or frog Viterbi_1.py uses a classic

Riley Baker 1 Jan 18, 2022
Emotion Recognition from Facial Images

Reconhecimento de Emoções a partir de imagens faciais Este projeto implementa um classificador simples que utiliza técncias de deep learning e transfe

Gabriel 2 Feb 09, 2022
Dataset used in "PlantDoc: A Dataset for Visual Plant Disease Detection" accepted in CODS-COMAD 2020

PlantDoc: A Dataset for Visual Plant Disease Detection This repository contains the Cropped-PlantDoc dataset used for benchmarking classification mode

Pratik Kayal 109 Dec 29, 2022
Yolov5 deepsort inference,使用YOLOv5+Deepsort实现车辆行人追踪和计数,代码封装成一个Detector类,更容易嵌入到自己的项目中

使用YOLOv5+Deepsort实现车辆行人追踪和计数,代码封装成一个Detector类,更容易嵌入到自己的项目中。

813 Dec 31, 2022
The official implementation of Variable-Length Piano Infilling (VLI).

Variable-Length-Piano-Infilling The official implementation of Variable-Length Piano Infilling (VLI). (paper: Variable-Length Music Score Infilling vi

29 Sep 01, 2022
Portfolio asset allocation strategies: from Markowitz to RNNs

Portfolio asset allocation strategies: from Markowitz to RNNs Research project to explore different approaches for optimal portfolio allocation starti

Luigi Filippo Chiara 1 Feb 05, 2022
A keras implementation of ENet (abandoned for the foreseeable future)

ENet-keras This is an implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, ported from ENet-training (lua-t

Pavlos 115 Nov 23, 2021
Semantic Segmentation of images using PixelLib with help of Pascalvoc dataset trained with Deeplabv3+ framework.

CARscan- Approach 1 - Segmentation of images by detecting contours. It failed because in images with elements along with cars were also getting detect

Padmanabha Banerjee 5 Jul 29, 2021
A library that can print Python objects in human readable format

objprint A library that can print Python objects in human readable format Install pip install objprint Usage op Use op() (or objprint()) to print obj

319 Dec 25, 2022
Official PyTorch Implementation of Convolutional Hough Matching Networks, CVPR 2021 (oral)

Convolutional Hough Matching Networks This is the implementation of the paper "Convolutional Hough Matching Network" by J. Min and M. Cho. Implemented

Juhong Min 70 Nov 22, 2022
PyTorch Kafka Dataset: A definition of a dataset to get training data from Kafka.

PyTorch Kafka Dataset: A definition of a dataset to get training data from Kafka.

ERTIS Research Group 7 Aug 01, 2022
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising By Tengfei Liang, Yi Jin, Yidong Li, Tao Wang. Th

workingcoder 115 Jan 05, 2023
pytorch implementation of Attention is all you need

A Pytorch Implementation of the Transformer: Attention Is All You Need Our implementation is largely based on Tensorflow implementation Requirements N

230 Dec 07, 2022
Pacman-AI - AI project designed by UC Berkeley. Designed reflex and minimax agents for the game Pacman.

Pacman AI Jussi Doherty CAP 4601 - Introduction to Artificial Intelligence - Fall 2020 Python version 3.0+ Source of this project This repo contains a

Jussi Doherty 1 Jan 03, 2022
Attention-driven Robot Manipulation (ARM) which includes Q-attention

Attention-driven Robotic Manipulation (ARM) This codebase is home to: Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation I

Stephen James 84 Dec 29, 2022
This repo contains the implementation of YOLOv2 in Keras with Tensorflow backend.

Easy training on custom dataset. Various backends (MobileNet and SqueezeNet) supported. A YOLO demo to detect raccoon run entirely in brower is accessible at https://git.io/vF7vI (not on Windows).

Huynh Ngoc Anh 1.7k Dec 24, 2022
Train a state-of-the-art yolov3 object detector from scratch!

TrainYourOwnYOLO: Building a Custom Object Detector from Scratch This repo let's you train a custom image detector using the state-of-the-art YOLOv3 c

AntonMu 616 Jan 08, 2023