Welcome to RuNNer!
The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-Parrinello-type high-dimensional neural network potentials.
Version breaking changes in RuNNer 1.3
Be careful, with the new version we have introduced some breaking changes:
Some keywords are now deprecated and need to be removed from existing
- Construction of very flexible neural network potentials for the
representation of potential energies in high-dimensional systems:
- 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 systems.
- several types of descriptors for the atomic environments, including atom-centered 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).
- Evaluation of the NNPs in junction with a MD or MC run.