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 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 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.
An application for evaluating thermodynamic quantities and phase diagrams of alloys and compounds. This application can calculate thermal-equilibrium phase diagrams and thermodynamic quantities of alloys and compounds in combination with databases, and can be utilized for evaluation and prediction of physical properties in materials science and metallurgy. It supports various models of thermodynamics, and also includes useful tools for plotting phase diagrams.
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.
2DMAT is a framework for applying a search algorithm to a direct problem solver to find the optimal solution. In version 1.0, for solving a direct problem, 2DMAT offers the wrapper of the solver for the total-reflection high-energy positron diffraction (TRHEPD) experiment. As algorithms, it offers the Nelder-Mead method, the grid search method, the Bayesian optimization method, and the replica exchange Monte Carlo method. Users can define original direct problem solvers or the search algorithms.
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.
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 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.
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.