ab-initio-simulations

Deep neural networks for density functional theory Hamiltonian.

Quantum Visualization Interacting Toolkit for Ab-initio Simulations

A solver for the coupled and decoupled electron and phonon Boltzmann transport equations.

Tight-binding package written in Julia

Extended DeepH (xDeepH) method for magnetic materials.

Quantum Monte Carlo package, TurboRVB

Sparse Gaussian Process Potentials

Open-source first-principles computational toolkit for the efficient calculation of the strength of materials in 1D, 2D, and 3D materials at both zero and finite temperatures

A modern DFT + DMFT computation framework

jQMC code implements two real-space ab initio quantum Monte Carlo (QMC) methods. Variatioinal Monte Carlo (VMC) and lattice regularized diffusion Monte Carlo (LRDMC) methods. jQMC achieves high-performance computations especially on GPUs.

SHRY (Suite for High-throughput generation of models with atomic substitutions implemented by python) is a tool for generating unique ordered structures corresponding to a given disordered structure.

Provides basic data structures and helpful functions for manipulating structures, generating input files, pre-running error checks, etc.

Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.

Python wrappers for TurboRVB

`orbkit` is a JAX-compatible toolkit for continuous ab initio quantum Monte Carlo (QMC) simulations, developed entirely from scratch using Python and JAX.

Hop.jl has been renamed to HopTB.jl and moved to https://github.com/HopTB/HopTB.jl

VASP input files required to conduct ab initio MD simulation of a short polyurethane chain.

Psi4 based Potential energy surface for 1D/2D/4D collisions; TensorFlow (neural networks) Augmenting; Curve Fitting And Multipole Expansion with GUI Interface

A Julia package that provides operations of a database with pseudopotential datasets

Study of molecular motion of Glycerol using NMR modeling and simulations