Inverse design of photonic surfaces on Inconel via multi-fidelity machine learning ensemble framework and high throughput femtosecond laser processing
This work addresses the problem of designing efficient photonic surfaces for energy harvesting applications, representing an incremental advancement through a novel method for a known bottleneck.
The researchers tackled the inverse design of photonic surfaces for energy harvesting by developing a multi-fidelity machine learning ensemble framework, achieving high accuracy with root mean squared errors below 2% and validating it experimentally.
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.