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.

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GASP

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

An application for structure prediction based on the genetic algorithm. This application can predict the structure and composition of stable phase of crystals, molecules, atomic clusters, and so on by using first-principles calculation and molecular dynamics. This application implements interfaces with various programs such as VASP, LAMMPS, MOPAC, GULP, JDFTx, etc, and runs efficiently on parallel computing architectures.

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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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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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DC

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

An electronic state solver distributed with GAMESS, the quantum chemical (QM) calculation software. Combining energy density analysis and Divide-and-Conquer (DC) method, accurate QM calculation with electronic correlation is solved in a short time. Highly accurate QM calculations for many-atom/nano-scale material can be solved when run on a high performance super computer.

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Avogadro

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

An open-source application of molecular modeling/editing for quantum chemical calculation. This application supports graphical user interface (GUI) for input-file preparation for software of quantum chemical calculation such as GAMESS, Gaussian, etc., and displays their results by reading output files. It can also make movies in the formats of vector graphics, POV-Ray, and so on.

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SPRKKR

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

A open-source application of first-principles calculation for the electronic structure, using the KKR method, a variant of Green’s function method. It is based on the density functional theory and is applicable to crystals and surfaces. The coherent potential approximation (CPA) is adopted, so it can handle not only periodic systems, but also disordered alloys. It can also handle spin-orbit interaction and non-collinear magnetism.

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TURBOMOLE

  • Level of openness 0 ☆☆☆
  • Document quality 2 ★★☆

Payware for the ab-initio quantum chemical calculation. This application preforms high-speed electronic structure calculation by introducing the RI approximation, and evaluates not only ground states but also excited states by various methods such as full RPA, TDDFT, CIS(D), CC2, ADC(2). It can also be used for evaluation of spectra data of infrared(IR), visible(Vis)/ultraviolet(UV), Raman, and circular dichroism spectroscopy.

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Blueqat

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

Open-source Python code for simulation of gate-type quantum computers. Blueqat can call Qiskit, a quantum computing development tool, to run IBM Q, a gate-type quantum computer.

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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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