Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns.

Area of Science:

• Materials Science
• Laser Physics
• Computational Physics

Background:

• Ultrafast laser irradiation induces spontaneous surface self-organization into nanoscale dissipative structures.
• These patterns arise from symmetry-breaking dynamics akin to Rayleigh-Bénard instabilities.

Purpose of the Study:

• To numerically unravel the coexistence and competition of different surface pattern symmetries in 2D.
• To develop a predictive model for laser-induced surface self-organization.

Main Methods:

• Utilized the stochastic generalized Swift-Hohenberg model for numerical simulations.
• Proposed a novel deep convolutional neural network (CNN) for pattern identification.
• Employed a physics-guided machine learning strategy for model calibration with microscopy data.

Main Results:

• Successfully identified and learned dominant modes stabilizing specific surface patterns.
• Demonstrated scale-invariance and validated the CNN against experimental microscopy data.
• The approach accurately predicts pattern formation from sparse, non-time-series data.

Conclusions:

• The developed deep learning approach enables the prediction of desired self-organization patterns.
• This method allows for the identification of experimental conditions for controlled material structuring.
• Paves the way for supervised local matter manipulation using controlled optical fields in laser manufacturing.