A set of python modules for modeling atomic structures, running simulations, and visualizing results. These modules provide interfaces for various application of first-principles calculation, classical molecular dynamics, and quantum chemical calculation through GUI, command line, or python scripts. The source code is available under the LGPL.
aenet is software for atomic interaction potentials using artificial neural networks. Users can construct neural network potentials using structures of target materials and their energies obtained from first principle calculations. The generated potentials can be used to molecular dynamics or Monte Carlo simulations.
Open-source package for first-principles calculation based on pseudo-potential and plane-wave basis. This package performs various electronic-state calculation by density functional theory such as band calculation of solids, and structure optimization of surfaces/interfaces. Detailed tutorials and documents are well prepared in this package, and many physical quantities including chemical reaction and lattice vibration can be obtained easily.
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.
AkaiKKR is a first-principles all-electron code package that calculates the electronic structure of condensed matters using the Green’s function method (KKR). It is based on the density functional theory and is applicable to a wide range of physical systems. It can be used to simulate not only periodic crystalline solids, but also used to calculate electronic structures of impurity systems and, by using the coherent potential approximation (CPA), random systems such as disordered alloys, mixed crystals, and spin-disordered systems.
ALPS is a numerical simulation library for strongly correlated systems such as magnetic materials or correlated electrons. It contains typicalsolvers for strongly correlated systems: Monte Carlo methods, exact diagonalization, the density matrix renormalization group, etc. It can be used to calculate heat capacities, susceptibilities, magnetization processes in interacting spin systems, the density of states in strongly correlated electrons, etc. A highly efficient scheduler for parallel computing is another improvement.
A program package for constructing interatomic force fields which explicitly consider lattice anharmonicity. In combination with a molecular dynamics simulator LAMMPS and an external first-principles package such as VASP and Quantum ESPRESSO, ALAMODE extracts harmonic/anharmonic force constants of solids and calculates phonon dispersion, phonon DOS, Gruneisen parameter, phonon-phonon scattering probability, lattice thermal-conductivity, anharmonic phonons at finite temperature, phonon free energy and so on.
A package for the auxiliary field Quantum Monte Carlo method, which enables us to calculate finite-temperature properties of the Hubbard-type model. It is also possible to treat the Hubbard model coupled to a transversed Ising field. Many examples such as Hubbard model on the square lattice and the honeycomb lattice are provided in the documentation.
AMULET is a collection of tools for a first principles calculation of physical properties of strongly correlated materials. It is based on density functional theory (DFT) combined with dynamical mean-field theory (DMFT). Users can calculate physical properties of chemically disordered compounds and alloys within CPA+DMFT formalism.
Software framework for training a machine learning model to reproduce first-principles energies and then using the model to perform configurational sampling in disordered systems. It has been developed with an emphasis on multi-component solid-state systems such as metal and oxide alloys. At present, Quantum Espresso, VASP and OpenMX can be used as first-principles energy calculators, and aenet can be used to construct neural network potentials.