Python wrapper to manage jobs for the ab initio Monte Carlo package TurboRVB. By combining with a workflow management application, TurboWorkflows, users can perform high-throughput calculations based on TurboRVB.
Easy-to-use and fast Python library for simulation of quantum information and quantum many-body systems. It provides Tensor module for tensor network simulations and Matrix module for “exact” quantum simulations.
TC++ is open-source software for ab initio calculations using the transcorrelated (TC) method. In TC++, users can take account of electron correlations in a Jastrow correlation factor based on the TC method. Electronic structures obtained by Quantum ESPRESSO can be used as an initial state of TC++.
BEEMs is a Bayesian optimization tool of Effective Models (BEEMs). In BEEMs, the quantum lattice model solver HΦ is used as a forward problem solver to compute the magnetisation curve based on the given Hamiltonian. The deviation between the obtained magnetisation curve and the target magnetisation curve is used as a cost function, and the Bayesian optimization library PHYSBO is used to propose the next candidate point of the Hamiltonian for searching the minimum cost function
H-wave is a Python package for performing unrestricted Hartree-Fock (UHF) calculations and random phase approximation (RPA) calculations for itinerant electron systems. H-wave supports UHF calculations both in real- and wavenumber-spaces. H-wave supports one-body and two-body interactions in the Wannier90 format as inputs for H-wave, and thus users can solve ab initio effective Hamiltonians derived from Wannier90/RESPACK calculations based on UHF and RPA methods.
peps-torch is a python library for calculation of quantum many-body problems on two dimensional lattices. Variational principles calculation is used with an infinite PEPS (iPEPS) as the trial wave function. Therefore, the ground state is obtained in the form of the element tensor of the iPEPS. The energy of the trial state is estimated by the corner transfer matrix method (CTM), and its gradient with respect to the element tensor is computed through automatic differentiation provided by pytorch. Functions/classes for exploiting the system’s symmetry are provided for reducing the computational cost if possible. While general models and lattices are not supported, many examples of stand-alone codes would make it relatively easy for users to write their own codes to suit their needs. pytorch is required.
A python package for automatic calculation of magnetic effective interactions between atoms (exchange and Dzyaloshinskii-Moriya interactions) from ab initio Hamiltonians based on Wannier functions and LCAO calculations. The package can postprocess Hamiltonians calculated using Wannier90, Siesta, and OpenMX. Input files for magnetic structure simulators such as Vampire can also be generated.
RESPACK is a first-principles calculation software for evaluating the interaction parameters of materials. It is able to calculate the maximally localized Wannier functions, the RPA response functions, and frequency-dependent electronic interaction parameters. RESPACK receives its input data from a band calculation using norm-conserving pseudopotentials with plane-wave basis sets. Utilities which convert a result of xTAPP or Quantum ESPRESSO to an input for RESPACK are prepared. The software has been used successfully for a wide range of materials such as metals, semiconductors, transition-metal compounds, and organic compounds. It supports OpenMP / MPI parallelization.
A Python library for simulating strongly correlated quantum systems using tensor networks. The goal is to make the algorithms readable and easy to use for beginners, and also powerful and fast for experts. Simple sample code and toy code to illustrate TEBD and DMRG are also provided.
An open source C++ library designed for the development of tensor network algorithms. The goal of this library is to provide basic tensor operations with an easy-to-use interface, and it also provides a Network class that handles the graphical representation of networks. A wrapper for calling it from Python is also provided.