Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing

Grbčić, Luka; Park, Minok; Elzouka, Mahmoud; Prasher, Ravi; Müller, Juliane; Grigoropoulos, Costas P.; Lubner, Sean D.; Zorba, Vassilia; de Jong, Wibe Albert

doi:10.1038/s41524-025-01518-4

Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing

Journal Article · Sat Feb 15 00:00:00 EST 2025 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-025-01518-4· OSTI ID:2528044

Grbčić, Luka ^[1]; ^[1]; Elzouka, Mahmoud ^[1]; Prasher, Ravi ^[2]; Müller, Juliane ^[3]; Grigoropoulos, Costas P. ^[2]; Lubner, Sean D. ^[4]; Zorba, Vassilia ^[2]; ^[1]

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Boston University, MA (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)

Grant/Contract Number:: AC02-05CH11231; AC36-08GO28308

Other Award/Contract Number:: 2107-1539

OSTI ID:: 2528044

Alternate ID(s):: OSTI ID: 2539818

Report Number(s):: NREL/JA--2C00-91627; ark:/13030/qt5kd9t2wr

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 11; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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97 MATHEMATICS AND COMPUTING
Optical materials and structures
Techniques and instrumentation
femtosecond laser processing
high-throughput experimentation
inverse design
multi-fidelity machine learning
optical materials and structures
photonic surface

Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing

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