Advancing Laser-Induced Nanoscale Surface Self-Organization with Machine Learning Guidance

Unraveling the emergence of spontaneous patterns on laser-irradiated materials has been a long-standing pursuit. Periodic surface structures manifest as a result of multiphysical coupling involving electromagnetics, nonlinear optics, plasmonics, fluid dynamics, and thermochemical reactions. Periodic surface structures result from multiphysical coupling: electromagnetics, nonlinear optics, plasmonics, fluid dynamics, and thermochemical reactions. Multi-shot ultrafast laser pulses generate stable periodic patterns influenced by disturbances and nonlinear saturation. Describing pattern growth requires a model with symmetry breaking, scale invariance, stochasticity, and nonlinear properties. Stochastic Swift-Hohenberg modeling replicates hydrodynamic fluctuations near the convective instability threshold in laser-induced self-organized nanopatterns. We demonstrate that deep convolutional networks can learn pattern complexity, connecting model coefficients to experimental parameters for specific pattern design. The model predicts patterns accurately, even with limited data, identifying laser parameter regions and predicting novel patterns independently.