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 support application for preparing input files of molecular dynamics calculation. This application supports manual input of atomic coordinates and bond informations, reading files of protain structure database, and editing data by graphical user interface. It also implements various functions such as addition of hydrogen atoms and composition of data. and can treat a large number of atoms using only a moderate memory cost.
An application for first-principles calculation by the joint-DFT method based on a plane-wave basis. By implementation of the joint-DFT method, this application realizes a good convergence for electronic state calculation of molecules in liquid, particular for charged systems. This application is written by C++11, and supports GPU calculation by CUDA. This application also supports diffusive Monte Carlo simulation in cooperation with CASINO.
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.
An application for structure prediction based on the genetic algorithm. This application can predict the structure and composition of stable phase of crystals, molecules, atomic clusters, and so on by using first-principles calculation and molecular dynamics. This application implements interfaces with various programs such as VASP, LAMMPS, MOPAC, GULP, JDFTx, etc, and runs efficiently on parallel computing architectures.
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 official Gaussian-series payware for molecular visualization. Must be used with Gaussian, the well-known software of quantum chemistry calculation. This application provides many functions such as molecular modeling, parameter setting, job management and visualization of calculation results. It also performs input file generation for Gaussian, and supports read/write of files with other formats such as Sybyl, Molden, PDB and CIF.
A payware for modeling and visualizing molecules. This software includes a standard editor, ChemDraw, and can perform modeling from chemical structural formula. It implements structure optimization and molecular dynamics by molecular mechanics, and provides useful GUIs for MOPAC, Jaguar, GAMESS, and Gaussian. It can also perform spectroscopy analysis. It is included in high-end packages such as ChemBioOffice and ChemOffice.
An application for first-principles calculation based on all-electron calculation using atomic bases. This application can perform accurate electronic-state calculation for various physical systems. It supports a number of functional sets including hybrid functionals, and can support relativistic effects, many-body perturbation methods, and the GW method. It can treat over 100 elements, and keeps high efficiency in parallel calculation from a desktop machine to a high-performance parallel computer up to 10,000 CPUs.
Open source software for building and using machine learning potentials based on E(3)-equivariant graph neural networks, which can be trained on output files of simulation codes that can be read by ASE. Molecular dynamics calculations with LAMMPS can be performed using the trained potentials.