In principle the library and all applications are agnostic to a specific system of physical units. This means that numeric values in input files are processed unaltered. Hence, it is the user’s responsibility to provide data in a consistent way. For instance, the same length units must be used in training configurations (see configuration file format) and in the definition of symmetry functions (mind that some parameters, e.g. \(\eta, r_c\), need to be given in length units).


Note that unit conversion may be required if an existing neural network potential is used to drive an MD simulation in LAMMPS. If the LAMMPS units (see command units) are not matching with those used during NNP training, appropriate conversion factors need to be provided. See the pair_style nnp reference for further details.

Normalizing the data set: “internal” units

Processing data sets unaltered potentially introduces a dependence of training results on the chosen unit system, i.e. if the same data set would be set up with different physical unit systems, it is unclear whether the training would converge to comparable errors. To avoid this problem an additional pre-processing of the training data can be performed with the tool nnp-norm. This tool will determine conversion factors for a reduced “internal” unit system and add them to the settings file. Other tools will recognize the corresponding keywords and automatically perform the conversion to “internal” units. No additional intervention of the user is necessary and quantities are usually converted back to physical units for screen or file output.


Sometimes quantities are provided also in internal units (for debugging purposes). If this is the case it will be explicitly mentioned in the screen output or in the file header. The default output of all tools is given in the original physical unit system.

For further details see the tool description and a recent publication 1.


The described data set normalization step is optional, you are not required to perform it. It is also not guaranteed that the quality of training will increase in all cases.


Singraber, A.; Morawietz, T.; Behler, J.; Dellago, C. Parallel Multistream Training of High-Dimensional Neural Network Potentials. J. Chem. Theory Comput. 2019, 15 (5), 3075–3092. https://doi.org/10.1021/acs.jctc.8b01092