  In recent years, many types of machine learning potentials (MLPs) have been
introduced, which are able to represent high-dimensional potential-energy
surfaces (PES) with close to first-principles accuracy. Most current MLPs rely
on atomic energy contributions given as a function of the local chemical
environments. Frequently, in addition to total energies also atomic forces are
used to construct the potentials, as they provide detailed local information
about the PES. Since many systems are too large for electronic structure
calculations, obtaining reliable reference forces from smaller subsystems like
molecular fragments or clusters can substantially simplify the construction of
the training sets. Here, we propose a method to determine structurally
converged molecular fragments providing reliable atomic forces based on an
analysis of the Hessian. The method, which serves as a locality test and allows
to estimate the importance of long-range interactions, is illustrated for a
series of molecular model systems and the metal-organic framework MOF-5 as an
example for a complex organic-inorganic hybrid material.
