  The analysis of the damage on plasma facing materials (PFM), due to its
direct interaction with the plasma environment, is needed to build the next
generation of nuclear machines, where tungsten has been proposed as a
candidate. In this work, we perform molecular dynamics (MD) simulations using a
machine learned inter-atomic potential, based on the Gaussian Approximation
Potential framework, to model better neutron bombardment mechanisms in pristine
W lattices. The MD potential is trained to reproduce realistic short-range
dynamics, the liquid phase, and the material recrystallization, which are
important for collision cascades. The formation of point defects is quantified
and classified by a descriptor vector (DV) based method, which is independent
of the sample temperature and its constituents, requiring only modest
computational resources. The locations of vacancies are calculated by the
k-d-tree algorithm. The analysis of the damage in the W samples is compared to
results obtained by EAM Finnis-Sinclair and Tersoff-ZBL potentials, at a sample
temperature of 300 K and a primary knock-on atom (PKA) energy range of 0.5-10
keV, where a good agreement with the reported number of Frenkel pair is
observed. Our results provide information about the advantages and limits of
the machine learned MD simulations with respect to the standard ones. The
formation of dumbbell and crowdion defects as a function of PKA is discussed.
