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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NequIP

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

Open source software for building and using machine learning potentials based on E(3)-equivariant graph neural networks, which can be trained on output files of simulation codes that can be read by ASE. Molecular dynamics calculations with LAMMPS can be performed using the trained potentials.

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Allegro

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

Open source software for constructing the Allegro potential model based on E(3)-equivariant graph neural networks and using the potential model for molecular dynamics simulations. The code depends on NequIP and can be run in a similar manner. Allegro scales better than NequIP since it doesn’t rely on message passing and the architecture is strictly local with respect to atom-wise environments.

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pacemaker

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

Software tool for constructing interatomic potentials based on nonlinear atomic cluster expansion. It requires the user to either prepare a fitting dataset based on pandas and ASE, or it can automatically extract data from VASP calculation results. The obtained potentials can be used for molecular dynamics simulations using LAMMPS, and it also provides the capability to calculate extrapolation grades for on-the-fly active learning.

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H-wave

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

H-wave is a Python package for performing unrestricted Hartree-Fock (UHF) calculations and random phase approximation (RPA) calculations for itinerant electron systems. H-wave supports UHF calculations both in real- and wavenumber-spaces. H-wave supports one-body and two-body interactions in the Wannier90 format as inputs for H-wave, and thus users can solve ab initio effective Hamiltonians derived from  Wannier90/RESPACK calculations based on UHF and RPA methods.

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BEEMs

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

BEEMs is a Bayesian optimization tool of Effective Models (BEEMs). In BEEMs, the quantum lattice model solver HΦ is used as a forward problem solver to compute the magnetisation curve based on the given Hamiltonian. The deviation between the obtained magnetisation curve and the target magnetisation curve is used as a cost function, and the Bayesian optimization library PHYSBO is used to propose the next candidate point of the Hamiltonian for searching the minimum cost function

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LibTetraBZ

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

Libtetrabz is a library which perform efficiently the Brillouin-zone integration in the electronic structure calculation in a solid by using the tetrahedron method.

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MultiPie

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

A Python library for manipulating  symmetry operations and automatically generating symmetry-adapted multipole basis (SAMB) based on crystallographic point and space groups. By using QtDraw, users can also visualize the output of this library.

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TC++

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

TC++ is open-source software for ab initio calculations using the transcorrelated (TC) method. In TC++, users can take account of electron correlations in a Jastrow correlation factor based on the TC method. Electronic structures obtained by Quantum ESPRESSO can be used as an initial state of TC++.

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OQMD: The Open Quantum Materials Database

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

A database for thermodynamic properties and crystal structures calculated based on the density functional theory by a research group in Northwestern University. OQMD provides over one million data generated by using not only experimental crystal structures provided by ICSD but also those obtained by calculations. Users can search data in OQMD by using Python API.

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