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

On the classification and quantification of crystal defects after energetic bombardment by machine learned molecular dynamics simulations