Solving two-dimensional spin models with tensor networks (powered by PyTorch)

Gaussian processes and Bayesian optimization for images and hyperspectral data

PyTorch library for full counting statistics, NEGF transport, counting-field derivatives, and lattice quantum transport calculations

TeNeT is a tensor-network renormalization code for the statistical mechanics of lattice models. The main application is adsorption of molecules on surfaces.

A demo to simulate 2D spin lattices with different shapes, boundary conditions, models and algorithms

The Python implementation of an exact solution for the hard hexagon model, proposed by Baxter in 1980. It is a 2D lattice model of a gas, where particles are allowed to be on the vertices of a triangular lattice but no two particles may be adjacent.

Qualitative and statistically grounded comparison of growth sampling and Metropolis Monte Carlo methods for three-dimensional self-avoiding polymer chains on a cubic lattice.

Splitting methods for semi-classical Hamiltonian dynamics of charge transfer in nonlinear lattices

VibeSpin is a Python framework for simulating and analyzing 2D lattice spin systems (Ising, XY, and q-state Clock models) with Numba-accelerated Monte Carlo dynamics, correlation/structure diagnostics, and reproducible benchmarking workflows.

In silico study of voting bloc emergence through allelomimetic behavior.