An open-source numerical library for machine learning. Using other machine learning numerical libraries (TensorFlow, CNTK, Theano, etc.), users can construct neural networks by relatively short codes. Since a number of methods in machine learning and deep learning are implemented, users can try state-of-the-art methods easily. This package is written by Python.
An application for first-principles calculation based on the all-electron method. This application implements not only normal electronic state calculation (band calculation) but also a quasi-particle GW method for self-consistent (or one-shot) calculation of excitation spectrum and quasi-particle band. Combining with dynamical mean-field theory, self-consistent calculation including many-body effect can also be performed.
An open-source application for quantum chemical calculation. This package implements various methods for quantum chemical calculation such as Hartree-Fock approximation, density functional theory, coupled-cluster method, and CI (configuration interaction) method. The package is written in C++, and provides API for Python, by which users can perform for preparation of setting and execution of calculation.
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.
ComDMFT is a massively parallel computational package to study the electronic structure of correlated-electron systems. Users can perform a parameter-free method based on ab initio linearized quasiparticle self-consistent GW (LQSGW) and dynamical mean field theory (DMFT).
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.
A program package for numerically solving effective lattice models using matrix product states (MPS). The ground state of a one-dimensional quantum system and its time evolution can be numerically evaluated by using an infinite system algorithm based on MPS. Useful tutorials and examples of calculations are also provided.