An application for first-principles calculation based on the all-electron method. This application implements not only normal electronic state calculation (band calculation) but also a quasi-particle GW method for self-consistent (or one-shot) calculation of excitation spectrum and quasi-particle band. Combining with dynamical mean-field theory, self-consistent calculation including many-body effect can also be performed.
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.
Software package that implements Behler-Parinello type neural network potential. The package provides tools for training and evaluating potentials based on given structure-energy data. It also provides an interface with LAMMPS for performing molecular dynamics calculations.
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.
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 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.
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.
Open source software for constructing the Allegro potential model based on E(3)-equivariant graph neural networks and using the potential model for molecular dynamics simulations. The code depends on NequIP and can be run in a similar manner. Allegro scales better than NequIP since it doesn’t rely on message passing and the architecture is strictly local with respect to atom-wise environments.
Software tool for constructing interatomic potentials based on nonlinear atomic cluster expansion. It requires the user to either prepare a fitting dataset based on pandas and ASE, or it can automatically extract data from VASP calculation results. The obtained potentials can be used for molecular dynamics simulations using LAMMPS, and it also provides the capability to calculate extrapolation grades for on-the-fly active learning.
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.