Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing
Abstract 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 gener...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01518-4 |
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| _version_ | 1850196830477877248 |
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| author | Luka Grbčić Minok Park Mahmoud Elzouka Ravi Prasher Juliane Müller Costas P. Grigoropoulos Sean D. Lubner Vassilia Zorba Wibe Albert de Jong |
| author_facet | Luka Grbčić Minok Park Mahmoud Elzouka Ravi Prasher Juliane Müller Costas P. Grigoropoulos Sean D. Lubner Vassilia Zorba Wibe Albert de Jong |
| author_sort | Luka Grbčić |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-7e7385e3d8e44b50888f80cbb62d27d0 |
| institution | OA Journals |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-7e7385e3d8e44b50888f80cbb62d27d02025-08-20T02:13:20ZengNature Portfolionpj Computational Materials2057-39602025-02-0111111310.1038/s41524-025-01518-4Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processingLuka Grbčić0Minok Park1Mahmoud Elzouka2Ravi Prasher3Juliane Müller4Costas P. Grigoropoulos5Sean D. Lubner6Vassilia Zorba7Wibe Albert de Jong8Applied Mathematics and Computational Research Division, Computing Science Area, Lawrence Berkeley National LaboratoryEnergy Storage and Distributed Resources Division, Energy Technologies Area, Lawrence Berkeley National LaboratoryEnergy Storage and Distributed Resources Division, Energy Technologies Area, Lawrence Berkeley National LaboratoryEnergy Storage and Distributed Resources Division, Energy Technologies Area, Lawrence Berkeley National LaboratoryComputational Science Center, National Renewable Energy Laboratory, GoldenEnergy Storage and Distributed Resources Division, Energy Technologies Area, Lawrence Berkeley National LaboratoryEnergy Storage and Distributed Resources Division, Energy Technologies Area, Lawrence Berkeley National LaboratoryEnergy Storage and Distributed Resources Division, Energy Technologies Area, Lawrence Berkeley National LaboratoryApplied Mathematics and Computational Research Division, Computing Science Area, Lawrence Berkeley National LaboratoryAbstract 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.https://doi.org/10.1038/s41524-025-01518-4 |
| spellingShingle | Luka Grbčić Minok Park Mahmoud Elzouka Ravi Prasher Juliane Müller Costas P. Grigoropoulos Sean D. Lubner Vassilia Zorba Wibe Albert de Jong Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing npj Computational Materials |
| title | Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing |
| title_full | Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing |
| title_fullStr | Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing |
| title_full_unstemmed | Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing |
| title_short | Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing |
| title_sort | inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing |
| url | https://doi.org/10.1038/s41524-025-01518-4 |
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