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.
CONQUEST is a linear-scaling DFT (Density Functional Theory) code based on the density matrix minimization method. Since its computational cost, for both memory and computational costs, is only proportional to the number of atoms N of the target systems, the code can employ structure optimization or molecular dynamics on very large-scale systems, including more than hundreds of thousands of atoms. It also has high parallel efficiency and is suitable for massively parallel calculations.
Code for unfolding first-principles electronic energy bands calculated using supercells into the corresponding primary-cell Brillouin zone. It uses maximally-localized Wannier functions calculated using Wannier90.
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.
An open-source application for first-principles calculation based on pseudopotential and wavelet basis. Electronic state calculation of massive systems is performed with high accuracy and high efficiency by using adaptive mesh. Parallel computing by MPI, OpenMP, and GPU is also supported.
Library for calculating Pfaffian (square root of determinant), which is defined for skew-symmetric matrices. Algorithms are implemented in several languages (Fortran, Python, Matlab, Mathematica) and users can choose favorite one. Interfaces for C are also provided.
i-PI is a universal force engine interface written in Python, designed to be used together with an ab-initio (or force-field based) evaluation of the interactions between the atoms. This application includes a large number of sophisticated methods such as replica exchange molecular dynamics (REMD) and path integral molecular dynamics (PIMD). Inter-atomic forces can be computed by using external codes such as CP2K, Quantum ESPRESSO and LAMMPS.
Open source Python package for data mining of materials. It can extract data from more than dozens of databases, perform preprocessing and visualization of extracted data. By combining machine-learning tools such as scikit-learn, users can build machine-learning models with descriptors created from the extracted data.
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.
Application for performing first-principles simulations with an implicit solvent model. The code is released as a patch to VASP. The user can perform molecular dynamics as well as reaction analysis using e.g., nudged elastic band method.