Python library to compute properties of quantum tight binding models, including topological, electronic and magnetic properties and including the effect of many-body interactions.

User-friendly open-source software to design and solve tight-binding models, addressing electronic properties, topology, interactions, non-collinear magnetism, and unconventional superconductivity, among others.

2D time-dependent Ginzburg-Landau in Python

Predict the Critical Temperature of a Superconductor

Simulate the magnetic response of 2D superconductors

Svirl is GPU-accelerated solver of complex Ginzburg-Landau equations for superconductivity. It consists of time-dependent solver to describe vortex dynamics and free energy minimizer to accurately find static configurations.

AiiDA plugin of the high-performance density functional theory code JuKKR (www.judft.de) for high-throughput electronic structure calculations.

Parameterized verification framework (PCells, DRC, LVS) for Superconducting and Quantum Electronics.

SuperDetectorPy is a program for simulating superconducting single-photon detectors.

Composition-based predictions for chemically novel, high-temperature superconductors.

Numerical solver for DC Bi-SQUID/SQUID circuits.

Simulate general non-equilibrium superconductor/ferromagnet systems via the Usadel diffusion equation

Implementation of gauged string method for the calculation of minimum energy path of vortex nucleation in superconductors within Ginzburg-Landau theory.

Data processing pipeline for analysis of Shubnikov de Haas oscillations

Package for solving generalized BdG mean field theory of interacting systems.

Simulate specific equilibrium superconductor/ferromagnet systems via the Usadel diffusion equation

Full-potential Korringa-Kohn-Rostoker Green function code JuKKR: All-electron DFT (repo mirror)

Teaching materials of the school "Emergent phenomena in van der Waals heterostructures", TIFR 2023

This solves the Bogoliubov-de Gennes equations and gap equations in the s-wave superconductor with the use of the Reduced-Shifted Conjugate-Gradient Method method. See, Y. Nagai, Y. Shinohara, Y. Futamura, and T. Sakurai,[arXiv:1607.03992v2 or DOI:10.7566/JPSJ.86.014708]. http://dx.doi.org/10.7566/JPSJ.86.014708

Data repository for paper https://arxiv.org/abs/2112.09735