  Metal-organic frameworks (MOFs) are an incredibly diverse group of highly
porous hybrid materials, which are interesting for a wide range of possible
applications. For a reliable description of many of their properties accurate
computationally highly efficient methods, like force-field potentials (FFPs),
are required. With the advent of machine learning approaches, it is now
possible to generate such potentials with relatively little human effort. Here,
we present a recipe to parametrize two fundamentally different types of
exceptionally accurate and computationally highly efficient machine learned
potentials, which belong to the moment-tensor and kernel-based potential
families. They are parametrized relying on reference configurations generated
in the course of molecular dynamics based, active learning runs and their
performance is benchmarked for a representative selection of commonly studied
MOFs. For both potentials, comparison to a random set of validation structures
reveals close to DFT precision in predicted forces and structural parameters of
all MOFs. Essentially the same applies to elastic constants and phonon band
structures. Additionally, for MOF-5 the thermal conductivity is obtained with
full quantitative agreement to single-crystal experiments. All this is possible
while maintaining a high degree of computational efficiency, with the obtained
machine learned potentials being only moderately slower than the extremely
simple UFF4MOF or Dreiding force fields. The exceptional accuracy of the
presented FFPs combined with their computational efficiency has the potential
of lifting the computational modelling of MOFs to the next level.
