A numerical library for machine learning. Various functions on machine learning (including supervised learning and unsupervised learning) are implemented in this package. Complex network can be expressed in a simple form by using data flow graphs. Efficient CPU/GPGPU parallel computation is supported to realise efficient operation on large scale data.
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.
An open-source numerical library for machine learning. Various functions related to deep learning are implemented. This package directly treats equations as such, and have useful routines such as matrix operation and auto partial derivative. Users can convert their codes into C language, and can compile it. High speed operation by GPGPU parallel calculation is supported. A number of tutorials are available.
An open-source application for molecular dynamics. This application can perform molecular dynamics simulation of biopolymers and solvents consisting of a number of molecules/atoms. It implements a number of force field sets and algorithms, and supports parallel computing based on OpenMP. Java graphical user interface (GUI) is also included.
An open-source program package for numerical diagonalization of quantum spin systems. The FORTRAN source programs are relatively simple and highly readable, and it can be applied to various quantum spin systems by modifying the main routine. Both the Lanczos and the inverse iteration methods are implemented for calculation of eigenvalues and eigenvectors, as well as correlation functions. Can be also used for diagonalization problems of general sparse matrices.
A first principles calculation program using all electron mixture based approach. It targets broad physical systems such as isolated systems, surfaces and interfaces, and crystals, and it calculates all electronic states from core electrons to valence electrons. It deals with calculation methods such as the GW method, and also deals with parallel calculations. It can execute with high accuracy molecular dynamics calculations for electronic excited states based on time dependent density functional theory.
An open-source numerical library for machine learning. Various functions related to deep learning based on neural networks are implemented. Users can implement complex network with flexible description, and can try various state-of-the-art methods. This package is used in a number of companies in the world. This package is written by the script language, lua.
A library collection for numerical calculation of interacting quantum systems. Modern programming techniques are used in this library to implement common tasks for solving quantum impurity problems in dynamic mean-field theory in a simple and efficient way. It is written in C++ and Python, and includes tutorials using Jupyter Notebook.
An open-source solver for the impurity problem based on the continuous-time quantum Monte Carlo method. Imaginary-time Green’s functions of the impurity Anderson model and the effective impurity model in the dynamical mean-field approximation can be calculated with high speed by using an efficient Monte Carlo algorithm. The main programs are written by C++, and can be called from Python scripts.
An interface tool for combining first-principles calculation based on density functional theory (DFT) and TRIQS, the application for dynamical mean-field theory (DMFT). By combining Wien2k and TRIQS, self-consistent DFT+DMFT calculation can be realized by this tool. One-shot DFT+DMFT calculation using band structures obtained by other first-principles applications is also possible.