MateriApps LIVE!

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

Debian Live Linux System that contains OS, editors, materials science application software, visualization tools, etc. An environment needed to perform materials science simulations is provided as a one package. By booting up on VirtualBox virtual machine, one can start simulations, such as the first-principles calculation, molecular dynamics, quantum chemical calculation, lattice model calculation, etc, immediately.

To Detail

OpenPhase

  • Level of openness 3 ★★★
  • Document quality 1 ★☆☆

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.

To Detail

CLUPAN

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

Program libraries for alloy modeling analysis using a cluster expansion method. Energy of alloy systems evaluated by other electronic state calculation libraries is used as an input, and atomic configuration effects are evaluated with the accuracy of a first principles calculation. Ground state structures, evaluation of thermodynamic quantities, equilibrium diagrams, disordering by temperature, etc. can be calculated with high accuracy.

To Detail

Uni10

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

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.

To Detail

kmos

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

Application for specifying and simulating lattice kinetic Monte Carlo models. It has been developed in the context of simulating heterogeneous catalysis. Models can be specified using provided python APIs or through a simple GUI.

To Detail

NequIP

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

Open source software for building and using machine learning potentials based on E(3)-equivariant graph neural networks, which can be trained on output files of simulation codes that can be read by ASE. Molecular dynamics calculations with LAMMPS can be performed using the trained potentials.

To Detail

NAMD

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

An open-source application for molecular dynamics simulation of biomolecules, especially designed for massively parallel computing. This package enables us to perform efficient parallel calculation on parallel computers ranging from 100 to 20,000 cores. For preparation of calculation and analysis of orbits, it uses visualization software VMD. It supports file formats compatible with other applications such as AMBER and CHARMM, and can be used on various computing environments.

To Detail

QMCSGF

  • Level of openness 3 ★★★
  • Document quality 1 ★☆☆

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.

To Detail

SPINPACK

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

A free software library for numerical diagonalization of quantum spin systems. Although the programs are based on TITPACK, they have been completely rewritten in C/C++ and several extensions have been added. It can handle, for example, the Heisenberg model, the Hubbard model, and the t-J model. This library supports dimension reduction of matrices exploiting symmetries, and it can run in parallel computing environments.

To Detail

TeNPy

  • Level of openness 3 ★★★
  • Document quality 2 ★★☆

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.

To Detail