  Machine Learning (ML) potentials such as Gaussian Approximation Potential
(GAP) have demonstrated impressive capabilities in mapping structure to
properties across diverse systems. Here, we introduce a GAP model for
low-dimensional Ni nanoclusters and demonstrate its flexibility and
effectiveness in capturing the energetics, structural diversity and
thermodynamic properties of Ni nanoclusters across a broad size range. Through
a systematic approach encompassing model development, validation, and
application, we evaluate the model's efficacy in representing energetics and
configurational features in low-dimensional regimes, while also examining its
extrapolative nature to vastly different spatiotemporal regimes. Our analysis
and discussion shed light on the data quality required to effectively train
such models. Trajectories from large scale MD simulations using the GAP model
analyzed with data-driven models like Graph Neural Networks (GNN) reveal
intriguing insights into the size-dependent phase behavior and
thermo-mechanical stability characteristics of porous Ni nanoparticles.
Overall, our work underscores the potential of ML models which coupled with
data-driven approaches serve as versatile tools for studying low-dimensional
systems and complex material dynamics.
