Submonolayer growth under a moving scanning tunneling microscopy-inspired mobility perturbation: A kinetic Monte Carlo study

Kinetic Monte Carlo simulations are used to examine how a moving, spatially localized mobility perturbation influences submonolayer growth morphology. Motivated by scanning tunneling microscopy, the perturbation is modeled as a Gaussian reduction of adatom hopping barriers that travel across the surface along a prescribed raster path. This idealized formulation provides a controlled setting for separating the temporal, spatial, and energetic components of the imposed mobility field. By suppressing intrinsic diffusion anisotropy and material-specific chemistry, the model isolates how a moving local mobility field influences island formation and morphology. The simulations show that scan velocity produces the strongest directional response, generating elongated island structures aligned with the scan trajectory. In contrast, changes in perturbation range and perturbation strength produce largely isotropic morphologies. Increasing perturbation strength leads to comparatively modest island-shape regularization through enhanced local mobility, whereas increasing perturbation range produces weaker restructuring associated with redistribution and aggregation. Conventional scalar observables, such as island density and mean island size, capture global aggregation trends but do not fully distinguish the underlying morphological pathways. Aspect-ratio and morphology-resolved analyses reveal that velocity, range, and strength act as distinct kinetic control parameters rather than as interchangeable measures of perturbation intensity. Scaling analysis further indicates that the moving perturbation modifies effective aggregation kinetics without substantially changing the coverage-dependent growth regime. These findings show that morphology-resolved analysis can distinguish the temporal, spatial, and energetic components of a moving local mobility perturbation even when conventional scalar growth measures show similar trends.