# FAQ

### I use only short range energies for the fit, but the RMSEs of the short range energies and the total energies are different. Shouldn't they be the same?

The RMSE of the total energy is always calculated using all training points. If for the fitting the keywords max_energy is used, only points with a lower energy are used for the fitting and the calculation of the RMSE of the short-range energy.

### What is the required system size for the training structures?

In the training set structures with different numbers of atoms and also periodic and non-periodic structures can be mixed as long as they are numerically consistent (in particular the k-point meshes in case of periodic structures need to be very well converged, Gamma point only calculations are usually not sufficient). Ideally the largest structures should have a diameter of at least two times the cutoff radius to avoid artificial periodicity in the atomic environments. Atoms close to the cutoff radius (but still inside) may, however, contribute only a little to the symmetry function values and thus may not be so important. It is therefore often not critical to use structures, which are somewhat smaller in diameter than two times the cutoff radius.

### I get extrapolation warnings. What can I do?

Extraplation warnings in runner_mode 3 indicate that one or more symmetry function values of one or more atoms are outside the respective interval(s) spanned by the training data. In this case, the potential may not be reliable. Extrapolation warnings may also be used to search for structures, which extend the range of values of the symmetry functions. Be aware that the absence of extrapolation warnings does not indicate that the training data is sufficiently dense close to the structure of interest and further tests may be required (e.g. comparing predictions of several independently constructed NNPs) to validate the potential.

### How can I validate the NNP for systems being too large for DFT calculations?

There are several options:

1. While the total system easily may become too large for a DFT calculation, the same local atomic environments often can often be represented in smaller systems, e.g. by cutting a cluster around the atom of interest. The forces acting on these atoms can be directly compared, but please be aware that formally the forces depend on the environment with twice the cutoff radius (see e.g. J. Behler, Angew. Chem. Int. Ed. 56 (2017) 12828.). For large cutoffs the true physical interaction range may be smaller of course.

2. A very cheap method, which does not require additional electronic structure calculation for validation, is to compare the prediction of two or more NNPs (see e.g. Fig. 11 Angew. Chem. Int. Ed. 56 (2017) 12828 or Fig. 3 Phys. Rev. B 85 (2012) 045439) regarding the predicted energies or better the atomic forces.

### In which cases does it make sense to use the mpi version of RuNNer?

Currently only runner_mode 3 is parallelized using mpi. So far we have found the Kalman filter to be most efficient, and it shows the optimum performance when updating the weights after each energy and force component, which makes an efficient parallelization of the optimization in runner_mode 2 very difficult. Still there is a trivial openmp parallelization of the symmetry function calculation, which can provide a speed-up of a factor of 2 in mode 2 (See the section on compiling RuNNer for details). It would in principle be possible to parallelize runner_mode1 efficiently, but only a minor part of the CPU time is spent here, which is why it has not been parallelized so far.

### I have the RMSE of the full training and test sets, but I want to analyze the error for specific subgroups of structures. Can this be done?

Yes, this is possible by restarting the fit with 0 epochs using the optimized weight files for the full data set (cf. keyword use_old_weights). Use then new data files generated using only the subgroup of structures you are interested in. For generating these data files you need to rerun RuNNer in runner_mode 1 for this subgroup first. You need to set also the keyword use_old_scaling and read in the original scaling.data file of the fit, because any change in the training set would change the range of symmetry functions and modify the fit you like to investigate. Please be aware that the scaling.data file is part of the fit. In particular in case the subgroup of structures contains less elements than the full training set, e.g. if the fit has been constructed for a ternary system but you are interested in a subgroup of binary structures, RuNNer would stop otherwise with an error due to a zero range of some symmetry function values. Still, also in case the chemical composition does not change, reading in the old scaling.data is mandatory even if you do not get an error message.

### My fits die after epoch 0 without a message

Please try to adjust the stack size of your terminal session using

ulimit -s unlimited


### Can I construct a potential using only energies, because I have no reference forces?

Yes, this is possible. Just enter 0.0 for all force components in the input.data file and make sure that in all modes the keyword use_short_forces is commented. If it would be active, RuNNer would assume that all forces are physically 0.0 and try to adjust the NN weights accordingly, which must be avoided.

### The numbers are slightly different on a different platform, why?

In some cases we have observed that the combination [rounding]{#rounding error problem} error problem ifort/mkl with high optimization settings (O3) can result in slightly different numbers depending on the version, in particular in runner_mode 2. RuNNer.To exclude this case,it can be fixed by adding -fp-model precise in FFLAGS from Makefile in RuNNer and run checks with lower optimization levels or using other blas/lapack libraries (e.g. from netlib). The gfortran compiler seems to be more robust in this respect.

### What is a good initial choice for the symmetry functions?

For a new system it is recommended to begin with symmetry functions of type 2 and type 3. For type 2 for each element pair a set of 5-6 radial functions with $$R_s=0$$ should be used. The first $$\eta$$ exponent should be zero (corresponding to the maximum extension of the function). The largest $$\eta$$ exponent should be chosen such that the turning point of $$e^{-\eta R_{ij}^2}\cdot f_c(R_{ij})$$ is at the minimum interatomic distance of that element pair present in the data set. The remaining $$\eta$$ values should be in between such that equidistant turning points are obtained. For the angular functions of type 3 use for each element triple $$\zeta=1,2,4,$$ and $$16$$ in combination with both $$\lambda=+1,-1$$ and always $$\eta=0$$. All symmetry functions should have the same cutoff, e.g. 6 Å. Starting from this first set, which should already provide a reasonable potential, variations can be used (e.g. shifted radial functions with $$R_s\ne 0$$ or a second set of contracted angular functions with $$\eta>0$$).

### Can I combine periodic and non-periodic structures in a single training set?

This is possible. Note that a numerical consistency between both sets of structures is of utmost importance, by e.g. using the same basis set. Also considering k-points properly is important.