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
YOLOv7 - Framework Beyond Detection

🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

JinTian 3k Jan 01, 2023
ISBI 2022: Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image.

Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Introduction This repository contains the PyTorch implem

25 Nov 09, 2022
In generative deep geometry learning, we often get many obj files remain to be rendered

a python prompt cli script for blender batch render In deep generative geometry learning, we always get many .obj files to be rendered. Our rendered i

Tian-yi Liang 1 Mar 20, 2022
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

15 Nov 18, 2022
Differentiable Wavetable Synthesis

Differentiable Wavetable Synthesis

4 Feb 11, 2022
Tooling for the Common Objects In 3D dataset.

CO3D: Common Objects In 3D This repository contains a set of tools for working with the Common Objects in 3D (CO3D) dataset. Download the dataset The

Facebook Research 724 Jan 06, 2023
TextureGAN in Pytorch

TextureGAN This code is our PyTorch implementation of TextureGAN [Project] [Arxiv] TextureGAN is a generative adversarial network conditioned on sketc

Patsorn 147 Dec 14, 2022
RefineMask (CVPR 2021)

RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features (CVPR 2021) This repo is the official implementation of RefineMask:

Gang Zhang 191 Jan 07, 2023
A practical ML pipeline for data labeling with experiment tracking using DVC.

Auto Label Pipeline A practical ML pipeline for data labeling with experiment tracking using DVC Goals: Demonstrate reproducible ML Use DVC to build a

Todd Cook 4 Mar 08, 2022
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection, AAAI 2021.

Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection This repository is an official implementation of the AAAI 2021 paper Co-mi

MEGVII Research 20 Dec 07, 2022
Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Yihong Sun 12 Nov 15, 2022
Cmsc11 arcade - Final Project for CMSC11

cmsc11_arcade Final Project for CMSC11 Developers: Limson, Mark Vincent Peñafiel

Gregory 1 Jan 18, 2022
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Molecular Sets (MOSES): A benchmarking platform for molecular generation models Deep generative models are rapidly becoming popular for the discovery

MOSES 656 Dec 29, 2022
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

Aladdin Persson 4.7k Jan 08, 2023
HomoInterpGAN - Homomorphic Latent Space Interpolation for Unpaired Image-to-image Translation

HomoInterpGAN Homomorphic Latent Space Interpolation for Unpaired Image-to-image Translation (CVPR 2019, oral) Installation The implementation is base

Ying-Cong Chen 99 Nov 15, 2022
Code of paper "CDFI: Compression-Driven Network Design for Frame Interpolation", CVPR 2021

CDFI (Compression-Driven-Frame-Interpolation) [Paper] (Coming soon...) | [arXiv] Tianyu Ding*, Luming Liang*, Zhihui Zhu, Ilya Zharkov IEEE Conference

Tianyu Ding 95 Dec 04, 2022
Centroid-UNet is deep neural network model to detect centroids from satellite images.

Centroid UNet - Locating Object Centroids in Aerial/Serial Images Introduction Centroid-UNet is deep neural network model to detect centroids from Aer

GIC-AIT 19 Dec 08, 2022
HistoKT: Cross Knowledge Transfer in Computational Pathology

HistoKT: Cross Knowledge Transfer in Computational Pathology Exciting News! HistoKT has been accepted to ICASSP 2022. HistoKT: Cross Knowledge Transfe

Mahdi S. Hosseini 5 Jan 05, 2023
MolRep: A Deep Representation Learning Library for Molecular Property Prediction

MolRep: A Deep Representation Learning Library for Molecular Property Prediction Summary MolRep is a Python package for fairly measuring algorithmic p

AI-Health @NSCC-gz 83 Dec 24, 2022
✂️ EyeLipCropper is a Python tool to crop eyes and mouth ROIs of the given video.

EyeLipCropper EyeLipCropper is a Python tool to crop eyes and mouth ROIs of the given video. The whole process consists of three parts: frame extracti

Zi-Han Liu 9 Oct 25, 2022