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.
A python package for the tight-binding method. PythTB supports tight-binding calculations of electronic structures and Berry phase in various kinds of systems. Users can use ab initio parameters obtained by Wannier90.
An application for ab initio quantum chemical calculation. This application can calculate molecular structures, chemical reactivity, frequency analysis, electron spectrum, and NMR spectrum with high accuracy. It implements the density functional theory, the Hartree-Fock(HF) method as well as recently developed methods such as the post-HF correlation method. It also has GUI for molecular modeling and a tool for preparation of input files.
An open-source application for obtaining optimized many-body wavefunctions expressed by matrix product states (MPS). By using a second-generation density matrix renormalization group (DMRG) algorithm, many-body wave functions can be efficiently optimized. The quantum-chemical operators are represented by matrix product operators (MPOs), which provides flexibility to accommodate various symmetries and relativistic effects.
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.
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.
Provides a complete set of environments necessary for computational materials science research in the cloud. A web browser is all that is needed to start a full range of first-principles simulations, including modeling, calculation, data storage, and analysis. RSDFT is used as the engine, and the lineup will be expanded in the future. Data can be shared within a group, and structural data from other software such as GAUSSIAN and VASP can be read.
A program package for electronic state calculations based on two-component relativistic quantum chemical theories. Several schemes and algorithms, which are specialized in calculations of molecules containing heavy elements, have been implemented. Single-point energies for ground and excited states, geometry optimizations, and molecular properties are available. Furthermore, the package can perform accurate calculations for molecules including many heavy atoms such as metal clusters with practical computational cost.
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.