The fragment molecular orbital (FMO) method can efficiently do quantum-mechanical calculations of large molecular systems by splitting the whole system into small fragments. The FMO program is distributed within quantum-chemical program suite GAMESS-US. FMO can provide various information regarding the structure and function of biopolymers, such as the interaction between a protein and a ligand.
Payware for ab initio quantum chemical calculation. This application performs high-speed quantum chemical calculation based on the density functional, Hartree-Fock theory, and MP2 theories. It can perform structure optimization, spectrum analysis, evaluation of acid dissociation constants, and so on. It can treat excited states by using TDDFT and CIS. Maestro, an application for visualization produced by the same developer, provides a useful interface for Jaguar.
An open-source application of semi-empirical/ab-initio quantum chemical calculation that comes under an academic license. It performs various quantum chemical calculations based on Hartree-Fock theory, density functional theory, and configuration interaction theory, yielding electronic states and enabling structure optimization and molecular spectrum analysis. Molecular dynamics calculation based on the QM/MM method is also possible by using this software in combination with GROMACS.
An open-source application for quantum chemical calculation based on the density-matrix renormalization group (DMRG). For systems with a number of atomic orbitals, low-lying energy eigenvalues can be calculated in high accuracy of order of 1kcal/mol. This application is suitable especially to calculation of multi-orbital systems with one-dimensional topology such as chain-like or circular-like configuration of orbits.
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.