Python-based simulations of chemistry framework (PySCF) is a general-purpose electronic structure platform written in Python. Users can perform mean-field and post-mean-field methods with standard Gaussian basis functions. This package also provides several interfaces to other software such as BLOCK and Libxc.
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.
Kω implements large-scale parallel computing of the shifted Krylov subspace method. Using Kω, dynamical correlation functions can be efficiently calculated. This application includes a mini-application for calculating dynamical correlation functions of quantum lattice models such as the Hubbard model, the Kondo model, and the Heisenberg model in combination with the quantum lattice solver of quantum many-body problems, HΦ.
An electronic state solver distributed with GAMESS, the quantum chemical (QM) calculation software. Combining energy density analysis and Divide-and-Conquer (DC) method, accurate QM calculation with electronic correlation is solved in a short time. Highly accurate QM calculations for many-atom/nano-scale material can be solved when run on a high performance super computer.
An open-source application for quantum chemical calculation. This package implements various methods for quantum chemical calculation such as Hartree-Fock approximation, density functional theory, coupled-cluster method, and CI (configuration interaction) method. The package is written in C++, and provides API for Python, by which users can perform for preparation of setting and execution of calculation.
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 application for molecular simulations. This application supports various methods such as classical and ab initio molecular dynamics, path integral simulations, replica exchange simulations, metadynamics, string method, surface hopping dynamics, QM/MM simulations, and so on. A hierarchical parallelization between molecular structures (replicas) and force fields (adiabatic potentials) enables fast and efficient computation.
QMCPACK is a modern high-performance open-source Quantum Monte Carlo (QMC) simulation code. Its main applications are electronic structure calculations of molecular, quasi-2D and solid-state systems. Variational Monte Carlo (VMC), diffusion Monte Carlo (DMC), orbital space auxiliary field QMC (AFQMC) and a number of other advanced QMC algorithms are implemented.
An open-source application for ab initio quantum chemical calculation. This application performs electronic structure calculation of molecules by the Hartree-Fock, density functional, many-body perturbation, configuration interaction theories, and so on. Even though this application is freeware, it succeeds in maintaining high-quality and high-performance codes by active development, and has a number of world-wide users. It histrically shares core programs with GAMESS-UK.
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.