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.
A collection of shell scripts for installing open-source applications and tools for computational materials science to macOS, Linux PC, cluster workstations, and major supercomputer systems in Japan. Major applications are preinstalled to the nation-wide joint-use supercomputer system at Institute for Solid State Physics, University of Tokyo by using MateriApps Installer.
An exact diagonalization package for a wide range of quantum lattice models (e.g. multi-orbital Hubbard model, Heisenberg model, Kondo lattice model). HΦ also supports the massively parallel computations. The Lanczos algorithm for obtaining the ground state and thermal pure quantum state method for finite-temperature calculations are implemented. In addition, dynamical Green’s functions can be calculated using Kω, which is a library of the shifted Krylov subspace method. It is possible to perform simulations for real-time evolution from ver. 3.0.
An open-source impurity solver based on the quantum Monte Carlo method. Thermal equilibrium states of interacting impurity systems, such as the impurity Anderson model, can be evaluated by the continuous-time hybridization-expansion quantum Monte Carlo method. It can be used as a solver of effective impurity models derived from the dynamical mean-field theory (DMFT) and can deal with multi-orbital models. This package supports parallel computation by MPI and is developed based on the ALPSCore library.
An open-source solver for the impurity problem based on the continuous-time quantum Monte Carlo method. Imaginary-time Green’s functions of the impurity Anderson model and the effective impurity model in the dynamical mean-field approximation can be calculated with high speed by using an efficient Monte Carlo algorithm. The main programs are written by C++, and can be called from Python scripts.
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.
ERmod is software for calculating the free energy in soft, molecular aggregate. This program rapidly and accurately calculates the free energy of binding of a molecule in the aggregate through combination of the molecular dynamics simulation and the energy-representation theory of solvation. The solubility of a molecule can be determined with ERmod in arbitrary solvent including supercritical fluid and ionic liquid. Assessment is also possible for the binding strength and site of a molecule in micelle, lipid membrane or protein.
A fast molecular dynamics simulator for ferroelectrics. This simulator can execute molecular dynamics calculations quickly by dealing with dipole interaction efficiently. It can simulate the physical property of microscopic ferroelectric thin film of tens of nanometers, which is important in FeRAM(Ferroelectric Random Access Memory), controlling the shapes and effects of inactivated layers.
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.
An open source C++ library designed for the development of tensor network algorithms. The goal of this library is to provide basic tensor operations with an easy-to-use interface, and it also provides a Network class that handles the graphical representation of networks. A wrapper for calling it from Python is also provided.