i-PI is a universal force engine interface written in Python, designed to be used together with an ab-initio (or force-field based) evaluation of the interactions between the atoms. This application includes a large number of sophisticated methods such as replica exchange molecular dynamics (REMD) and path integral molecular dynamics (PIMD). Inter-atomic forces can be computed by using external codes such as CP2K, Quantum ESPRESSO and LAMMPS.
DCA++ is a software framework to solve correlated electron problems with modern quantum cluster methods. This code provides a state of the art implementation of the dynamical cluster approximation (DCA) and its DCA+ extension. As the cluster solvers, DCA++ provides the continuous-time auxiliary field QMC (CT-AUX) , the continuous-time hybridization expansion (CT-HYB) restricted to single-site problems, the high temperature series expansion (HTS) and the exact diagonalization(ED).
w2dynamics is a hybridization-expansion continuous-time (CT-HYB) quantum Monte Carlo package, developed jointly in Wien and Würzburg. Users can calculate local two- and four-pointfermionic Green’s functions of multi-orbital impurity models. This application also provides DMFT Python code and an interface to wannier90 generated Hamiltonians.
Python-based simulations of chemistry framework (PySCF) is a general-purpose electronic structure platform written in Python. Users can perform mean-field and post-mean-field methods with standard Gaussian basis functions. This package also provides several interfaces to other software such as BLOCK and Libxc.
QMCPACK is a modern high-performance open-source Quantum Monte Carlo (QMC) simulation code. Its main applications are electronic structure calculations of molecular, quasi-2D and solid-state systems. Variational Monte Carlo (VMC), diffusion Monte Carlo (DMC), orbital space auxiliary field QMC (AFQMC) and a number of other advanced QMC algorithms are implemented.
NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques. Users can perform machine learning algorithms to find the ground-state of many-body Hamiltonians such as supervised learning of a given state and optimization of neural network states by using the variational Monte Carlo method.
AMULET is a collection of tools for a first principles calculation of physical properties of strongly correlated materials. It is based on density functional theory (DFT) combined with dynamical mean-field theory (DMFT). Users can calculate physical properties of chemically disordered compounds and alloys within CPA+DMFT formalism.
A solver program for two dimensional quantum lattice model based on a projected entangled pair state wavefunction and the corner transfer matrix renormalization group method.
This works on a massively parallel machine because tensor operations are OpenMP/MPI parallelized.
Software framework for training a machine learning model to reproduce first-principles energies and then using the model to perform configurational sampling in disordered systems. It has been developed with an emphasis on multi-component solid-state systems such as metal and oxide alloys. At present, Quantum Espresso, VASP and OpenMX can be used as first-principles energy calculators, and aenet can be used to construct neural network potentials.
Open-source software for building computational physics applications. Common C++ auxiliary modules required for various methods in computational physics such as the quantum Monte Carlo method are prepared. This software helps to build reusable codes and to reduce development time for complex computational science applications. It also supports parallel programming based on MPI or OpenMP.