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.
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 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.
Payware for the ab-initio quantum chemical calculation. This application preforms high-speed electronic structure calculation by introducing the RI approximation, and evaluates not only ground states but also excited states by various methods such as full RPA, TDDFT, CIS(D), CC2, ADC(2). It can also be used for evaluation of spectra data of infrared(IR), visible(Vis)/ultraviolet(UV), Raman, and circular dichroism spectroscopy.
Automatic generation tool for codes of tensor contraction. This tool can automatically generate codes of an optimal computing sequence for construction of a single tensor from a tensor network composed of tensors. Netcon algorithm proposed by Pfeifer et al. is used, and it is possible to search optimal solution quickly. Generated codes are available in Numpy and mptensor in Python.