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).
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.
This is a structure analysis program for solutes and solvents, based on the statistical mechanics theory of liquids. The program determines the solvent density distribution surrounding the solute, and calculates various physical values such as the solvation free energy, compressibility, and partial molar volume. The program implements a parallelized fast Fourier transform routine for large-scale parallel computing, and can analyze molecular functions such as the ligand binding affinity of proteins, that would be difficult using other methods.
An application for adding a function of the replica exchange method to the existing applications for molecular dynamics simulation such as MODYLAS, AMBER, and CHARMM. Without changing original programs of molecular dynamics, the replica exchange method can be implemented easily. This application also shows high performance in massive parallel computing by the K-computer.
An open source application implementing path-integral Monte Carlo method based on Stochastic Green function method. Finite temperature calculation of extended Bose Hubbard model and Heisenberg model with finite field can be treated. JSON and YAML formats are adopted for data I/O.
An open-source application for the phase-field simulations. This application treats many kinds of problems in materials science such as determination of phase diagrams, crystal growing, small structures accompanied by first-order transition, and so on. Its source code is open under the GPL, and is developed putting emphasis on its flexibility in the C++ language.
Open-source package for molecular dynamics simulation designed for biological macromolecules. This package can perform molecular dynamics simulation of biological macromolecules such as proteins, lipids, and nuclear acids as well as solutions by controlling temperature and pressure. This package can treat long-range interaction and free energy, and is designed for parallel computing.
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.
TeNPy is a Python library which enables simulations of strongly correlated quantum systems using tensor networks. The package aims to balance both the readability and usability for beginners with algorithmic power and fast development for experts. One can use mainly MPS algorithms such as DMRG on 1D and 2D systems and simulations of dynamics using TEBD.
An open source library for implementing tensor networks. It is developed based on TensorFlow and is designed to be easily used by experts in the field of machine learning as well as in the field of physics. In addition to TensorFlow, it includes wrappers for JAX, PyTorch, and Numpy.