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
MAGMA - a GPT-style multimodal model that can understand any combination of images and language

MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning Authors repo (alphabetical) Constantin (CoEich), Mayukh (Mayukh

Aleph Alpha GmbH 331 Jan 03, 2023
An implementation of based on pytorch and mmcv

FisherPruning-Pytorch An implementation of Group Fisher Pruning for Practical Network Compression based on pytorch and mmcv Main Functions Pruning f

Peng Lu 15 Dec 17, 2022
Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

DiscoGAN Official PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. Prerequisites Python 2.7

SK T-Brain 754 Dec 29, 2022
3D position tracking for soccer players with multi-camera videos

This repo contains a full pipeline to support 3D position tracking of soccer players, with multi-view calibrated moving/fixed video sequences as inputs.

Yuchang Jiang 72 Dec 27, 2022
This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)

CEDR This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR) introduced in the following paper

phoenix 3 Feb 27, 2022
Libraries, tools and tasks created and used at DeepMind Robotics.

dm_robotics: Libraries, tools, and tasks created and used for Robotics research at DeepMind. Package overview Package Summary Transformations Rigid bo

DeepMind 273 Jan 06, 2023
Manifold Alignment for Semantically Aligned Style Transfer

Manifold Alignment for Semantically Aligned Style Transfer [Paper] Getting Started MAST has been tested on CentOS 7.6 with python = 3.6. It supports

35 Nov 14, 2022
PyTorch implementation of Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose

Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose Release Notes The official PyTorch implementation of Neural View S

Angtian Wang 20 Oct 09, 2022
[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | 斗地主AI

[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning DouZero is a reinforcement learning framework for DouDizhu (斗地主), t

Kwai Inc. 3.1k Jan 04, 2023
A general framework for inferring CNNs efficiently. Reduce the inference latency of MobileNet-V3 by 1.3x on an iPhone XS Max without sacrificing accuracy.

GFNet-Pytorch (NeurIPS 2020) This repo contains the official code and pre-trained models for the glance and focus network (GFNet). Glance and Focus: a

Rainforest Wang 169 Oct 28, 2022
Deep learning based hand gesture recognition using LSTM and MediaPipie.

Hand Gesture Recognition Deep learning based hand gesture recognition using LSTM and MediaPipie. Demo video using PingPong Robot Files Pretrained mode

Brad 24 Nov 11, 2022
An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu

Meta Research 141 Jan 07, 2023
Koç University deep learning framework.

Knet Knet (pronounced "kay-net") is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. It supports GPU

1.4k Dec 31, 2022
Towards Part-Based Understanding of RGB-D Scans

Towards Part-Based Understanding of RGB-D Scans (CVPR 2021) We propose the task of part-based scene understanding of real-world 3D environments: from

26 Nov 23, 2022
PyTorch wrappers for using your model in audacity!

audacitorch This package contains utilities for prepping PyTorch audio models for use in Audacity. More specifically, it provides abstract classes for

Hugo Flores García 130 Dec 14, 2022
Fight Recognition from Still Images in the Wild @ WACVW2022, Real-world Surveillance Workshop

Fight Detection from Still Images in the Wild Detecting fights from still images is an important task required to limit the distribution of social med

Şeymanur Aktı 10 Nov 09, 2022
Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)

IGNN Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" [paper] [supp] Prepare datasets 1 Download training dataset

Shangchen Zhou 278 Jan 03, 2023
Geometric Algebra package for JAX

JAXGA - JAX Geometric Algebra GitHub | Docs JAXGA is a Geometric Algebra package on top of JAX. It can handle high dimensional algebras by storing onl

Robin Kahlow 36 Dec 22, 2022
Event-forecasting - Event Forecasting Algorithms With Python

event-forecasting Event Forecasting Algorithms Theory Correlating events in comp

Intellia ICT 4 Feb 15, 2022
Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Wonjong Jang 8 Nov 01, 2022