QDS

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  • Document quality 1 ★☆☆

QDS (Quantum Dynamics Simulator) is a program for computing magnetization curves and spectra of electron-spin resonance (ESR) in molecular magnets. Input data of this program can be magnetic interactions, the shape of a molecule, etc. Calculation is carried out with the combination of exact diagonalization, the quantum master equation, and the Kubo formula. It can be chosen whether the dissipation exists or not in the calculations of dynamical magnetization curves.

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QMAS

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  • Document quality 1 ★☆☆

QMAS is an ab-initio electronic-structure computational code package based on the projector augmented-wave (PAW) with a plane wave basis set. It computes electronic states and various physical properties efficiently with high precision for a wide range of physical systems. It provides geometry optimization, electronic states in a static magnetic field, permittivity distribution at the atomic-scale, energy and stress distribution, positron annihilation parameters, and so forth.

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QMCPACK

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

QMCPACK is a modern high-performance open-source Quantum Monte Carlo (QMC) simulation code. Its main applications are electronic structure calculations of molecular, quasi-2D and solid-state systems. Variational Monte Carlo (VMC), diffusion Monte Carlo (DMC), orbital space auxiliary field QMC (AFQMC) and a number of other advanced QMC algorithms are implemented.

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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.

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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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QTWARE

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

An application for evaluation of thermoelectric properties and its visualization. Seebeck coefficients and Peltier coefficients can be calculated from output of the first-principles applications, OpenMX and TranSIESTA. Obtained results as well as electron density and density of states can be visualized.

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QUANTUM ESPRESSO

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

Open-source program for first-principles calculation based on pseudo-potential and plane-wave basis. This package performs electronic-state calculation with high accuracy based on density functional theory. In addition to basic-set programs, many core-packages and plugins are included. This package can be utilized for academic research and industrial development, and also supports parallel computing.

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Questaal

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

An application for first-principles calculation based on the all-electron method. This application implements not only normal electronic state calculation (band calculation) but also a quasi-particle GW method for self-consistent (or one-shot) calculation of excitation spectrum and quasi-particle band. Combining with dynamical mean-field theory, self-consistent calculation including many-body effect can also be performed.

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Quimb

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

Easy-to-use and fast Python library for simulation of quantum information and quantum many-body systems. It provides Tensor module for tensor network simulations and Matrix module for “exact” quantum simulations.

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QUIP

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

A collection of software tools for molecular dynamics calculations. Various interatomic potentials and tight binding models are implemented, and numerous external applications can be invoked. It also supports training and evaluation of GAP (Gaussian Approximation Potential), which is a form of machine learning potential.

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