The RuNNer Code

In order to develop Neural Network potential-energy surfaces for a variety of system, we have developed our in-house NN code for high-dimensional systems called RuNNer, which was the first implementation of high-dimensional NN potentials. Much of the methodology has been originally developed with this code. The code is freely available under the GPL3 license. We currently host the source code in a repository at gitlab. If you are interested in obtaining a free copy of RuNNer, please write an email to joerg.behler@uni-goettingen.de stating your name, institution, official institutional email address and your gitlab username so that we can provide you access.


RuNNer has the following features:

• unlimited number of degrees of freedom (atoms)
• training data can be obtained from arbitrary electronic structure methods and codes
• training using energies and forces
• periodic and non-periodic system
• several types of symmetry functions are available
• several types of activation functions are available
• arbitrary topology of the atomic neural networks
• provides energies and analytic derivatives (forces and stress tensor)