Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns - Université Jean-Monnet-Saint-Étienne Access content directly
Journal Articles Physical Review Letters Year : 2023

Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns

Abstract

Ultrafast laser irradiation can induce spontaneous self-organization of surfaces into dissipative structures with nanoscale reliefs. These surface patterns emerge from symmetry-breaking dynamical processes that occur in Rayleigh-Bénard-like instabilities. In this study, we demonstrate that the coexistence and competition between surface patterns of different symmetries in two dimensions can be numerically unraveled using the stochastic generalized Swift-Hohenberg model. We originally propose a deep convolutional network to identify and learn the dominant modes that stabilize for a given bifurcation and quadratic model coefficients. The model is scale-invariant and has been calibrated on microscopy measurements using a physics-guided machine learning strategy. Our approach enables the identification of experimental irradiation conditions for a desired self-organization pattern. It can be generally applied to predict structure formation in situations where the underlying physics can be approximately described by a self-organization process and data is sparse and nontime series. Our Letter paves the way for supervised local manipulation of matter using timely controlled optical fields in laser manufacturing.
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Dates and versions

ujm-04157829 , version 1 (14-09-2023)

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Eduardo Brandao, Anthony Nakhoul, Stefan Duffner, R Emonet, Florence Garrelie, et al.. Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns. Physical Review Letters, 2023, 130, pp.226201. ⟨10.1103/physrevlett.130.226201⟩. ⟨ujm-04157829⟩
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