Ab initio quantum Monte Carlo solver for both molecular and bulk electronic systems. By using the geminal/Pfaffian wavefunction with the Jastrow correlator as the trial wavefunction, users can perform highly accurate variational calculations, structural optimizations and ab initio molecular dynamics for both classical and quantum nuclei.
Open Chemistry database that has been in operation since 2004 under the National Institutes of Health (NIH) in the United States. It mainly targets data for small molecules, but information on large molecules such as lipids and peptides are also collected. The database can be accessed via web browser or PUG REST API. The data can be also downloaded from an FTP site.
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.
TC++ is open-source software for ab initio calculations using the transcorrelated (TC) method. In TC++, users can take account of electron correlations in a Jastrow correlation factor based on the TC method. Electronic structures obtained by Quantum ESPRESSO can be used as an initial state of TC++.
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 that implements moment tensor potentials. Potentials can be trained and used for molecular dynamics calculations using LAMMPS. Active learning combined with molecular dynamics calculations is also available.
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.
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.
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.