A highly efficient framework for crystal structure exploration and property prediction dedicated to material science calculations. This application can automate the setup, execution, and analysis of the results of calculations based primarily on the density functional theory. It provides data on more than millions of crystal structures and can be used for high throughput calculations for material exploration. It also interfaces with various DFT codes (VASP, Quantum ESPRESSO, etc.).
Python tool for automatic extraction of chemical substance information from literature. Based on natural language processing algorithms, it can extract substance names and related physical/chemical properties such as melting points and spectra from documents written in English.
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.
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.
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.
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.
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.
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 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.
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.