Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing
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| Publikašuvnnas: | NPJ Computational Materials vol. 11, no. 1 (2025), p. 35 |
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| Almmustuhtton: |
Nature Publishing Group
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract Full Text - PDF |
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| 001 | 3167234515 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2057-3960 | ||
| 024 | 7 | |a 10.1038/s41524-025-01518-4 |2 doi | |
| 035 | |a 3167234515 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274866 |2 nlm | ||
| 245 | 1 | |a Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications. | |
| 653 | |a Energy harvesting | ||
| 653 | |a Laser processing | ||
| 653 | |a Local optimization | ||
| 653 | |a Photonics | ||
| 653 | |a Machine learning | ||
| 653 | |a Design optimization | ||
| 653 | |a Spectral emissivity | ||
| 653 | |a Emissivity | ||
| 653 | |a Lasers | ||
| 653 | |a Process parameters | ||
| 653 | |a Inverse design | ||
| 653 | |a Accuracy | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Nanoparticles | ||
| 653 | |a Optimization | ||
| 653 | |a Optical properties | ||
| 653 | |a Energy | ||
| 653 | |a Ablation | ||
| 653 | |a Mechanical engineering | ||
| 773 | 0 | |t NPJ Computational Materials |g vol. 11, no. 1 (2025), p. 35 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3167234515/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3167234515/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |