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

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.

Learning PDE to Model Self-Organization of Matter.

A self-organization hydrodynamic process has recently been proposed to partially explain the formation of femtosecond laser-induced nanopatterns on Nickel, which have important applications in optics, microbiology, medicine, etc. Exploring laser pattern space is difficult, however, which simultaneously (i) motivates using machine learning (ML) to search for novel patterns and (ii) hinders it, because of the few data available from costly and time-consuming experiments. In this paper, we use ML to predict novel patterns by integrating partial physical knowledge in the form of the Swift-Hohenberg (SH) partial differential equation (PDE). To do so, we propose a framework to learn with few data, in the absence of initial conditions, by benefiting from background knowledge in the form of a PDE solver. We show that in the case of a self-organization process, a feature mapping exists in which initial conditions can safely be ignored and patterns can be described in terms of PDE parameters alone, which drastically simplifies the problem. In order to apply this framework, we develop a second-order pseudospectral solver of the SH equation which offers a good compromise between accuracy and speed. Our method allows us to predict new nanopatterns in good agreement with experimental data. Moreover, we show that pattern features are related, which imposes constraints on novel pattern design, and suggest an efficient procedure of acquiring experimental data iteratively to improve the generalization of the learned model. It also allows us to identify the limitations of the SH equation as a partial model and suggests an improvement to the physical model itself.

Boosted Spontaneous Formation of High-Aspect Ratio Nanopeaks on Ultrafast Laser-Irradiated Ni Surface.

The capacity to synthesize and design highly intricated nanoscale objects of different sizes, surfaces, and shapes dramatically conditions the development of multifunctional nanomaterials. Ultrafast laser technology holds great promise as a contactless process able to rationally and rapidly manufacture complex nanostructures bringing innovative surface functions. The most critical challenge in controlling the growth of laser-induced structures below the light diffraction limit is the absence of external order associated to the inherent local interaction due to the self-organizing nature of the phenomenon. Here high aspect-ratio nanopatterns driven by near-field surface coupling and architectured by timely-controlled polarization pulse shaping are reported. Electromagnetic coupled with hydrodynamic simulations reveal why this unique optical manipulation allows peaks generation by inhomogeneous local absorption sustained by nanoscale convection. The obtained high aspect-ratio surface nanotopography is expected to prevent bacterial proliferation, and have great potential for catalysis, vacuum to deep UV photonics and sensing.

Self-Organization Regimes Induced by Ultrafast Laser on Surfaces in the Tens of Nanometer Scales.

A laser-irradiated surface is the paradigm of a self-organizing system, as coherent, aligned, chaotic, and complex patterns emerge at the microscale and even the nanoscale. A spectacular manifestation of dissipative structures consists of different types of randomly and periodically distributed nanostructures that arise from a homogeneous metal surface. The noninstantaneous response of the material reorganizes local surface topography down to tens of nanometers scale modifying long-range surface morphology on the impact scale. Under ultrafast laser irradiation with a regulated energy dose, the formation of nanopeaks, nanobumps, nanohumps and nanocavities patterns with 20-80 nm transverse size unit and up to 100 nm height are reported. We show that the use of crossed-polarized double laser pulse adds an extra dimension to the nanostructuring process as laser energy dose and multi-pulse feedback tune the energy gradient distribution, crossing critical values for surface self-organization regimes. The tiny dimensions of complex patterns are defined by the competition between the evolution of transient liquid structures generated in a cavitation process and the rapid resolidification of the surface region. Strongly influencing the light coupling, we reveal that initial surface roughness and type of roughness both play a crucial role in controlling the transient emergence of nanostructures during laser irradiation.