Understanding spontaneous pattern emergence on laser-irradiated materials is a long-standing interest. Periodic surface structures arise from multiphysical coupling: electromagnetics, nonlinear optics, plasmonics, fluid dynamics, or thermochemical reactions. Multi-shot irradiation with ultrafast laser pulses generates stable periodic patterns arising from localized perturbations influenced by disturbances and nonlinear saturation. Describing pattern growth requires nonlinear dynamics beyond classic equations. The challenge is developing an efficient model with symmetry breaking, scale invariance, stochasticity, and nonlinear properties to reproduce dissipative structures. Stochastic Swift-Hohenberg modeling replicates hydrodynamic fluctuations near the convective instability threshold, inherent in laser-induced self-organized nanopatterns. We will demonstrate that a deep convolutional networks can learn pattern complexity, connecting model coefficients to experimental parameters for designing specific patterns. The model predicts patterns accurately, even with limited non-time series data. It identifies laser parameter regions and could predict novel patterns independently.
Ultrafast-laser irradiated surface is a typical paragon of a self-organizing system, which emerge and organize complex micropatterns and even nanopatterns. An astounding exhibition of dissipative structures consists of various types of randomly and periodically generated nanostructures that originate from a homogeneous metal surface. The formation of nanopeaks, nanobumps, nanohumps and nanocavities patterns with 20–80 nm transverse size unit and up to 100 nm height are reported under femtosecond laser irradiation with a regulated energy dose. We shed the light on the originality of the nanopeaks, having an exceptional aspect ratio on the nanoscale. They are primarily generated on the crests grown between the convective cells formed on the very first pulses. The production of these distinct nanostructures can enable unique surface functionalizations toward the control of mechanical, biomedical, optical, or chemical surface properties on a nanometric scale.
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