An application for molecular science simulation. This application covers not only traditional simulation methods implemented in existing applications but also a number of novel methods for quantum chemical calculation. It can perform ab-initio electronic state calculation for a few thousands atoms/molecules as well as trace calculation of transition states in chemical reaction for a few hundreds atoms/molecules. It can also perform high-efficient massively parallel computing on large-scale parallel computers such as the K-computer.
An open-source application for molecular dynamics simulation of biomolecules, especially designed for massively parallel computing. This package enables us to perform efficient parallel calculation on parallel computers ranging from 100 to 20,000 cores. For preparation of calculation and analysis of orbits, it uses visualization software VMD. It supports file formats compatible with other applications such as AMBER and CHARMM, and can be used on various computing environments.
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.
A group of applications that perform molecular dynamics, hybrid quantum/classical mechanical simulation, search of chemical reaction path by the nudged elastic band method, and potential parameter fitting. The molecular dynamics code includes interatomic potentials for several metals and semiconductors, and is capable of parallel computation based of spatial decomposition.
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.
NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques. Users can perform machine learning algorithms to find the ground-state of many-body Hamiltonians such as supervised learning of a given state and optimization of neural network states by using the variational Monte Carlo method.