CIF2Cell is a tool to generate a crystal structure part of an input file of first-principles calculation software from crystal structure data file in CIF format. It supports various first-principles calculation codes such as ABINIT, Quantum Espresso, and VASP.
Ab initio quantum Monte Carlo solver for both molecular and bulk electronic systems. By using the geminal/Pfaffian wavefunction with the Jastrow correlator as the trial wavefunction, users can perform highly accurate variational calculations, structural optimizations and ab initio molecular dynamics for both classical and quantum nuclei.
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.
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.
First-principles software based on plane-wave basis and norm-conserving pseudopotential methods. Time-dependent DFT has been implemented. Users can perform real-time simulations for electron-ion dynamics under a time-dependent external field. Pseudopotentials with FPSEID21 format should be used, and those are downloadable from the website.
A python package for automatic calculation of magnetic effective interactions between atoms (exchange and Dzyaloshinskii-Moriya interactions) from ab initio Hamiltonians based on Wannier functions and LCAO calculations. The package can postprocess Hamiltonians calculated using Wannier90, Siesta, and OpenMX. Input files for magnetic structure simulators such as Vampire can also be generated.
A python library for pre- and post-processing of first-principles electronic structure calculations. As a pre-processing tool, it can automatically generate k-point pathways for first-principles calculations of band structures based on the crystal symmetry. It can also post-process first-principles calculation results to generate band structure and density of states plots with atomic species and orbital contributions, or visualize spin textures and Fermi surfaces. It also provides a functionality for band unfolding.
Software package that implements Behler-Parinello type neural network potential. The package provides tools for training and evaluating potentials based on given structure-energy data. It also provides an interface with LAMMPS for performing molecular dynamics calculations.
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.
Python/C++ based software package that employs deep learning techniques for construction of interatomic potentials. It implements the Deep Potential, which defines atomic environment descriptors with respect to a local reference frame. The output of many first-principles and molecular dynamics applications can be used as training data, and the trained potentials can be used for molecular dynamics calculations using LAMMPS and path integral molecular dynamics calculations using i-PI.