  We assess Gaussian process (GP) regression as a technique to model
interatomic forces in metal nanoclusters by analysing the performance of
2-body, 3-body and many-body kernel functions on a set of 19-atom Ni cluster
structures. We find that 2-body GP kernels fail to provide faithful force
estimates, despite succeeding in bulk Ni systems. However, both 3- and
many-body kernels predict forces within a $\sim$0.1 eV/$\text{\AA}$ average
error even for small training datasets, and achieve high accuracy even on
out-of-sample, high temperature, structures. While training and testing on the
same structure always provides satisfactory accuracy, cross-testing on
dissimilar structures leads to higher prediction errors, posing an
extrapolation problem. This can be cured using heterogeneous training on
databases that contain more than one structure, which results in a good
trade-off between versatility and overall accuracy. Starting from a 3-body
kernel trained this way, we build an efficient non-parametric 3-body force
field that allows accurate prediction of structural properties at finite
temperatures, following a newly developed scheme [Glielmo et al. PRB 97, 184307
(2018)]. We use this to assess the thermal stability of Ni$_{19}$ nanoclusters
at a fractional cost of full ab initio calculations.
