ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Neural Network Enabled Real-Time Plasma Imaging for Inverse Designed Fabrication of Micro/Nano Structures with Ultrafast Laser.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Enhancing Aluminum Cutting Quality Through XGBoost-Assisted Optimization of Ultrafast Femtosecond Laser Processing.Micromachines · 2026Article
- Polarization-Controlled Femtosecond Laser Texturing Enables Robust Antifouling Stainless Steel Surfaces.Molecules (Basel, Switzerland) · 2026Article
- Pixel-level metal blackbody microcavities via hierarchical laser writing.Science advances · 2025Article
- Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one-to-many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root-mean-squared-error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser-matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.
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Registered trials
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