superconductors

GDSHelpers is an open-source package for automatized pattern generation for nano-structuring.

Simulate the magnetic response of 2D superconductors

Superconductors material dataset

Grobid module for superconductor material and properties extraction

Object Oriented toolbox for general tight binding models.

Material parsers and other tools, scripts Initially developed for Grobid Superconductor

Desktop app for lithography learning available for Windows, macOS, and Linux

Spectral Solver for the Ginzburg-Landau equation

Implementation of Diffusion Models for Conditional Generation of Hypothetical New Families of Superconductors

Staging-area for automatically collected experimental data for the SuperCon database with a curation interface with enhanced-document viewer and curation-ready interface

Superconductivity is a phenomenon where a charge can move through a material without any resistance. This allows electricity to be conducted at maximum efficiency. TensorFlow was used to make an accurate Neural Network.

Training a GAN using superconductivity data

A term paper discussing the history, developments and applications of High Temperature Superconductors

A research paper detailing the model building process of principal component regression using mathematical notation and a demonstration using the superconductivity dataset from the UCI machine learning repository.

A website to quickly plot PPMS data and find the Critical current (Ic) of an IV curve. deployed at https://share.streamlit.io/iamashwin99/jj-ic-finder/main/app.py and https://jj-ic-finder.herokuapp.com/

Solver for hybrid devices composed of quantum dots and superconducting islands

Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors

Predicting critical temperature of Superconductors using multiple linear regression and XGBoost methods

Notes for anyone who is interested in quickly familiarizing themselves with superconductors from a materials science perspective