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Neural Network potentials have a number of advantages:

- high numerical accuracy in reproducing the total energies of the reference ab initio calculations
- no physical approximations needed
- no 'ad hoc' introduction of arbitrary energy terms necessary
- analytic gradients for the calculation of forces and the stress tensor are available
- evaluation of Neural Network energies and forces is very fast (about 100 atoms per core per second), typically 4-5 orders of magnitude faster than DFT at the GGA level
- in contrast to force fields, no classification of atoms and/or bonds is needed, Neural Network potentials are intrinsically "reactive"
- input has the same form as for electronic structure calculations: atomic positions and nuclear charges
- all types of bonding are treated in the same way (covalent, metallic, vdW etc.), no system-dependent adaptions of the functional form is required
- any electronic structure method can be used as reference method, a coupling to many quantum chemistry codes is easily possible
- very efficient parallelization

- because of the high flexibility a large number of reference calculations is needed
- formally no extrapolation capabilities (a potential made just for bulk will not work for clusters, but of course potentials can be trained to both)
- the accuracy of the Neural Network potential is limited by the accuracy of the underlying electronic structure method
- currently, the number of different chemical elements is limited to 3-4, but the number of atoms of each element can be very large

Because of these advantages and disadvantages, Neural Network potentials are most useful to speed up molecular dynamics simulations, if complex bonding situations or chemical reactions are present which cannot be described accurately by conventional potentials. We believe that in such cases Neural Network potentials are the method of choice to perform long molecular dynamics simulations of large systems, which are not directly accessible by ab initio MD.

We are constantly working on the further development of the Neural Network methodology to improve the accuracy and performance of these potentials. In order to construct Neural Network potentials for a variety of systems, we have written our own Neural Network package

For further details, please have a look at our

In the context of the research focus

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