libAtoms + QUIP
libAtoms is a software library package written in Fortran 95+ for the purposes of carrying out molecular dynamics simulations. QUIP is a package built on top of libAtoms that implements many types of interatomic potentials and several parametrisations of tight binding models. It also provides file-based methods for calling external packages including CASTEP, VASP, Molpro, CP2K. Most of it is licensed under GPLv2 with some other parts in the public domain.
Further details on libAtoms + QUIP
There is a publicly available version on GitHub
Gaussian Approximation Potentials (GAPs) are a new generation of interatomic potentials that interpolate the potential energy surface of atomistic systems in the high dimensional space of atomic configurations. Software to predict the potential energy using GAP models is available as an add-on to QUIP under an separate academic-only license. Clicking the link below will take you to a download page where you have to enter your name, email, and agree to an academic-only license in order to download the source code for the part of GAP that predicts potential energies. In order to build the code, you will first need to download libatoms and QUIP, then unarchive GAP under the QUIP directory.
To use GAP for a specific material, you need a data file containing set of atomic configurations and data to be interpolated. Some are available in our Data repository
QUIP potentials, including GAP, may be called from LAMMPS
. We maintain an interface, QUIPforLAMMPS
, which enables
We often use AtomEye
to view atomic configurations, and James Kermode has customized and extended it considerably. The modified version of AtomEye
can read NetCDF
files, with binaries for Linux and Mac OS X, and also copes with small unit cells.
is a Python environment, which provides wrapped version of many QUIP routines, as well as visualisation routines via AtomEye
. It also has excellent documentation which contains all of the libatoms+QUIP documentation as well.
Gaussian process interpolation
Given noisy observations of a real-valued function and/or its partial derivatives whose domain is D dimensional, an interpolation of the function is provided by a Gaussian process. This is used to reconstruct free energy surfaces from its gradients in a recently published work: T. Stecher, N. Bernstein and G. Csányi, "Free energy reconstruction from umbrella samples using Gaussian process regression"
. An example implementation of the code is available for download: GPR.tar.gz