Superconducting Toolkit (sctk)

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

An open-source application for evaluating superconducting gaps from resutls of the first-principles calculation by Quantum ESPRESSO. By calculating electron-phonon interaction and screened Coulomb interaction from the first-principles calculation, superconducting gaps can be obtained from the gap equation. Quasiparticle densities of states and ultrasonic attenuation rates can also be calculated.

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scikit-learn

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

An open-source library for data mining and data analysis. This package implements various methods of machine learning such as supervised learning (data classification, data regression, etc.), unsupervised learning (data clustering, etc.), and data pre-processing. This package is implemented on Python numerical libraries, NumPy and Scipy, and supports parallel computation.

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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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Strawberry Fields

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

Python library for the design, simulation, and optimization of continuous-variable quantum optical circuits. It has high-level functions for solving problems including graph and network optimization, machine learning, and chemistry, and can perform training and optimization of quantum programs using the TensorFlow backend.

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SHRY

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

A python tool for generating symmetry-inequivalent supercell structures from a CIF file containing site occupancy information. SHRY can be used as a command-line tool as well as a module in a python script.

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SIMPLE-NN

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

Software package to implement Behler-Parinello neural network potentials. Potentials can be trained from structure-energy/ interatomic forces/stress data, and molecular dynamics calculations using LAMMPS can also be performed using learned potentials. A prediction uncertainty measure can also be calculated simultaneously.

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