PLUMED

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open source library to calculate free energy in molecular dynamics simulation. It supports several famous molecular dynamics software packages such as Amber and Lammps.

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PyTorch

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An interface package to use Torch (the open-source numerical library for machine learning) from Python. Users can easily implement deep learning based on neural networks, and can use various state-of-the-art methods. This package supports GPGPU parallel computation, and realises high-speed operation. A front-end interface for C++ is also prepared.

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Octopus

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source application for first-principles calculation based on pseudo- potential and real-space basis. It performs electronic-state calculation such as band calculation of solids and structure optimization for a variety of physical systems. The method of time-dependent density functional theory (TDDFT) is implemented, which allows simulation of dynamical phenomena with real-time evolution of electronic states, such as chemical reaction and electronic response to time-dependent external fields. Comes with detailed tutorials and comprehensive manuals.

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POV-Ray

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An application for three-dimensional visualization with the ray tracing method. This application can visualize arbitrary positions and shapes of objects such as spheres and cubes. It can visualize three-dimensional data obtained from computational fluid dynamics etc. by volume rendering. It can also be used for simple three-dimensional graphical simulator with macro functions.

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Elastic

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

Elastic is a set of python routines for calculation of elastic properties of crystals (elastic constants, equation of state, sound velocities, etc.).  It is implemented as a extension to the Atomic Simulation Environment (ASE) system.  There is a script providing interface to the library not requiring knowledge of python or ASE system.

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DFTB+

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An application for quantum chemical calculation based on DFTB (Density Functional based Tight Binding). This application performs structure
optimization and molecular dynamics by the DFTB force field as well as ordinary energy calculation, and implements parallel computing by OpenMP. A tool for visualization of molecular orbitals and an extended versions supporting MPI parallel computation or electron transport calculation by the nonequilibrium Green’s function method are also
available.

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Gromacs

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

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.

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Caffe

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source library for machine learning. Various functions on deep learning based on neural network can be used by this package. This package is especially customised for image identification, and a number of sample codes are prepared. Users can also use pre-trained models, which are open in Caffe Model Zoo. Since this package is written in C++, high-speed operation is realised.

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QuTiP

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source application for dynamical simulation of open quantum systems. It supports a wide range of Hamiltonians such as quantum optics, ion traps, and superconducting circuits. The time evolution of quantum states is evaluated by a master equation. These calculation library can be called from Python via a user-friendly interface.

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Chainer

  • Level of openness 3 ★★★
  • Document quality 3 ★★★

An open-source library for machine learning. Various functions on machine learning/deep learning are implemented in this package. Using flexible user-friendly description, various types of networks from simple to complex ones can be implemented. GPGPU parallel computation based on CUDA is also supported.

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