peps-torch is a python library for calculation of quantum many-body problems on two dimensional lattices. Variational principles calculation is used with an infinite PEPS (iPEPS) as the trial wave function. Therefore, the ground state is obtained in the form of the element tensor of the iPEPS. The energy of the trial state is estimated by the corner transfer matrix method (CTM), and its gradient with respect to the element tensor is computed through automatic differentiation provided by pytorch. Functions/classes for exploiting the system’s symmetry are provided for reducing the computational cost if possible. While general models and lattices are not supported, many examples of stand-alone codes would make it relatively easy for users to write their own codes to suit their needs. pytorch is required.
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.
A python library for materials analysis. Flexible classes for representation of materials are prepared, and data for crystal structures and various material properties can be handled efficiently. This application can performs analysis of phase diagrams, Pourbaix diagrams, diffusion analyses etc. as well as electronic structure analyses such as density of states and band structures. This software is being actively developed keeping close relation with Materials Project.
QDS (Quantum Dynamics Simulator) is a program for computing magnetization curves and spectra of electron-spin resonance (ESR) in molecular magnets. Input data of this program can be magnetic interactions, the shape of a molecule, etc. Calculation is carried out with the combination of exact diagonalization, the quantum master equation, and the Kubo formula. It can be chosen whether the dissipation exists or not in the calculations of dynamical magnetization curves.
An open source application implementing path-integral Monte Carlo method based on Stochastic Green function method. Finite temperature calculation of extended Bose Hubbard model and Heisenberg model with finite field can be treated. JSON and YAML formats are adopted for data I/O.
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 application for adding a function of the replica exchange method to the existing applications for molecular dynamics simulation such as MODYLAS, AMBER, and CHARMM. Without changing original programs of molecular dynamics, the replica exchange method can be implemented easily. This application also shows high performance in massive parallel computing by the K-computer.
This is a structure analysis program for solutes and solvents, based on the statistical mechanics theory of liquids. The program determines the solvent density distribution surrounding the solute, and calculates various physical values such as the solvation free energy, compressibility, and partial molar volume. The program implements a parallelized fast Fourier transform routine for large-scale parallel computing, and can analyze molecular functions such as the ligand binding affinity of proteins, that would be difficult using other methods.
FORTRAN-based software package developed by the Behler Group for implementing Behler-Parinello neural network potentials. Potentials can be constructed, evaluated, and used for molecular dynamics simulations using LAMMPS. The newest generation of neural network potentials that take into account long-range electrostatic interactions are implemented.
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.