PAICS is a program of quantum chemical calculation. In this program, fragment molecular orbital (FMO) method is adopted, by which large molecules including biomolecular systems can be treated with several quantum chemical approaches including HF and MP2 methods. At the same time, PaicsView has been developed, which is a supporting program for making input files and analyzing calculation results.
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.
Open source software for massively parallel quantum chemistry calculations. Energies and geometries of nano-sized molecules can be calculated without fragmentation. The program supports Hartree-Fock, density functional theory, and second-order Møller-Plesset perturbation theory calculations. The input format, execution method, and program structure are simple, and frequently used routines can be easily extracted.
Open-source program for first-principles calculation based on pseudo-potential and plane-wave basis. This package performs electronic-state calculation with high accuracy based on density functional theory. In addition to basic-set programs, many core-packages and plugins are included. This package can be utilized for academic research and industrial development, and also supports parallel computing.
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.
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.
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 open-source application for first-principles molecular dynamics based on a pseudopotential method using plane bases. This application can perform electronic-state calculation and molecular dynamics employing the Car-Parrinello method. It implements MPI parallelization, which enables us to perform efficient parallel computing in various environments including large-scale parallel computers. The program is written in C++, and is distributed in source form under the GPL license.
An open-source application for general-purpose quantum chemical calculation, laying emphasis on excited states and time evolution. It is based on time-dependent density functional theory (TDDFT) and the QM/MM calculation. It enables efficient massive parallel computing up to one hundred thousands processes. It supports the relativistic effect and offers the basis choice between the Gaussian basis and the plane-wave basis.
xTAPP is a first-principles plane-wave pseudo-potential code. It computes band structure and electronic states with high precision for a wide range of materials including metals, oxide surfaces, solid interfaces, and so forth. It has support tools and visualization of output and input, is available as a massively parallel computer using OpenMP, MPI, and GPGPU.