  We present a metaheuristic conditional neural-network-based method aimed at
identifying physically interesting metastable states in a potential energy
surface of high rugosity. To demonstrate how this method works, we identify and
analyze spin textures with topological charge $Q$ ranging from 1 to $-13$
(where antiskyrmions have $Q<0$) in the Pd/Fe/Ir(111) system, which we model
using a classical atomistic spin Hamiltonian based on parameters computed from
density functional theory. To facilitate the harvest of relevant spin textures,
we make use of the newly developed Segment Anything Model (SAM). Spin textures
with $Q$ ranging from $-3$ to $-6$ are further analyzed using
finite-temperature spin-dynamics simulations. We observe that for temperatures
up to around 20\,K, lifetimes longer than 200\,ps are predicted, and that when
these textures decay, new topological spin textures are formed. We also find
that the relative stability of the spin textures depend linearly on the
topological charge, but only when comparing the most stable antiskyrmions for
each topological charge. In general, the number of holes (i.e.,
non-self-intersecting curves that define closed domain walls in the structure)
in the spin texture is an important predictor of stability -- the more holes,
the less stable is the texture. Methods for systematic identification and
characterization of complex metastable skyrmionic textures -- such as the one
demonstrated here -- are highly relevant for advancements in the field of
topological spintronics.
