Starrydata

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

Starrydata is an open database of experimental data from figures in published papers. Thermoelectric properties such as Seebeck coefficient, electrical resistivity and thermal conductivity are presented mainly on thermoelectric materials.

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PHYSBO (optimization tools for PHYsics based on Bayesian Optimization )

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

PHYSBO is a Python library for researchers mainly in the materials science field to perform fast and scalable Bayesian optimization based on COMBO (Common Bayesian Optimization). Users can search the candidate with the largest objective function value from candidates listed in advance by using machine learning prediction. PHYSBO can handle a larger amount of data compared with standard implementations such as scikit-learn.

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2DMAT

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

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.

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OpenMX Viewer

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

OpenMX Viewer (Open source package for Material eXplorer Viewer) is a web-based graphical user interface (GUI) program for visualization and analysis of crystalline and molecular structures.

XYZ, CIF, OpenMX input/output, md(molecular dynamics) files, the Gaussian cube format such as electron density and molecular orbitals can be visualized quickly by drag-and-drop, and it is easy to analyze static/dynamic structural properties conveniently in a web browser. Several basic functionalities such as analysis of Mulliken charges, molecular dynamics, geometry optimization and band structure are included.

 

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DAMASK

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

DAMASK is a unified multi-physics crystal plasticity simulation package. The solution of continuum mechanical boundary value problems requires a constitutive response that connects deformation and stress at each material point. This problem is solved in DAMASK on the basis of crystal plasticity using a variety of constitutive models and homogenization approaches. However, treating mechanics in isolation is no longer sufficient to study emergent advanced high-strength materials. In these materials, deformation happens interrelated with displacive phase transformation, significant heating, and potential damage evolution. Therefore, DAMASK is capable of handling multi-physics problems. Following a modular approach, additional field equations are solved in a fully coupled way using a staggered approach.

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QS3

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

An exact diagonalization package for efficiently solving quantum spin 1/2 lattice models in almost fully spin-polarized sectors. QS3 can treat such systems with quite large system sizes, over 1000 sites. It supports calculations of wavenumber-dependence of energy-dispersion and dynamical spin structure factor.

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PythTB

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

A python package for the tight-binding method. PythTB supports tight-binding calculations of electronic structures and Berry phase in various kinds of systems. Users can use ab initio parameters obtained by Wannier90.

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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.

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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.

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TensorNetwork

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

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.

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