The Atomic Energy Network
Atomic interaction potentials based on artificial neural networks.
The Atomic Energy Network (ænet) is a software package [1,2] for the construction and usage of atomic interaction potentials based on artificial neural networks (ANNs). In essence, ænet offers tools to train ANNs to the potential energy of atomic reference structures. In addition, the package provides C and Fortran libraries that can be integrated in existing simulation software to actually use ANN potentials in atomistic simulations.
ænet is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses.
Method and implementation
The ANN potential methodology was first suggested by Behler and Parrinello . The ænet code is a modern Fortran implementation of the method (with C interoperability) . Publications that made use of ænet for various applications can be found on the publications page.
We welcome other scientists to use ænet for their research and to contribute to the development of the package and the methodology.
Release notes and a link to the source code are on the download page.
If you run into problems with ænet or if you have a general question, please contact Dr. Nongnuch Artrith (email@example.com).
This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575.
 N. Artrith and A. Urban, Comput. Mater. Sci. 114 (2016) 135-150. (Editor’s Choice)
 N. Artrith, A. Urban, and Gerbrand Ceder, Phys. Rev. B 96 (2017) 014112.
 J. Behler and M. Parrinello, Phys. Rev. Lett. 98 (2007) 146401.