homcloud

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

A Python package for extracting structural features from point cloud and image data using the mathematical framework of persistent homology. In the field of materials science, it is used to characterize structural differences between liquids and glasses, as well as for dimensionality reduction of microscope images. It is also useful for obtaining structural descriptors for machine learning.

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HRC Experiment Support Web tools

  • Level of openness 3 ★★★
  • Document quality 1 ★☆☆
This web site provides web tools to support neutron scattering experiments at HRC spectrometer (BL12) in the Material and Lifescience Experimental Facility in J-PARC.
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IFEFFIT

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

An application for data analysis of X-ray absorption fine structure (XAFS). By interactive operation using a command line, experimental data of XAFS can be analyzed by various analysis methods. This application also supports various useful functions such as high-speed Fourier analysis, fitting in the radial/k-space coordinates, and data plotting.

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Inelastica

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

A pre/post-processing application for SIESTA and TranSIESTA. This application can calculate phonon frequencies, electron-phonon coupling, and contributions of inelastic scattering to the conductance. It also provides a Python interface for accessing data in the Hamiltonian output from SIESTA.

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k-ep

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

Fortran codes for computing the specified k-th eigenvalue and eigenvector for generalized symmetric definite eigenvalue problems. Sylvester’s law of inertia is employed as the fundamental principle in computations, and the sparse direct linear solver (MUMPS) is used in the main routine. By inputting Hamiltonian and its overlap matrices, user can compute electron’s energy and its wave function in the specified k-th energy level.

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LOBSTER

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

A post-processor of first-principles calculations for performing COHP (crystal orbital Hamilton population) and COOP (crystal orbital overlap population) chemical bonding analysis. It works with VASP, ABINIT and Quantum ESPRESSO output. The program is provided under an academic-only license.

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M2TD

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

This software is for constructing inter-atomic force fields that mostly fit the results of ab-initio calculations, using multi-canonical molecular dynamic simulations. Various potential functions such as silicon, ionic crystal, and water have been pre-installed, and the user’s potential function can also be used. The default ab initio calculation solver is xTAPP and other calculation libraries are also applicable.

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MateriApps Installer

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

A collection of shell scripts for installing open-source applications and tools for computational materials science to macOS, Linux PC, cluster workstations, and major supercomputer systems in Japan. Major applications are preinstalled to the nation-wide joint-use supercomputer system at Institute for Solid State Physics, University of Tokyo by using MateriApps Installer.

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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.

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matminer

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

Open source Python package for data mining of materials. It can extract data from more than dozens of databases, perform preprocessing and visualization of extracted data. By combining machine-learning tools such as scikit-learn, users can build machine-learning models with descriptors created from the extracted data.

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