Inverse design of photonic surfaces on Inconel via multi-fidelity machine learning ensemble framework and high throughput femtosecond laser processing

TL;DR

This paper presents a multi-fidelity machine learning ensemble framework for the inverse design of photonic surfaces, validated through high throughput femtosecond laser processing, achieving accurate spectral emissivity control for energy harvesting.

Contribution

It introduces a novel multi-fidelity ensemble approach combining low and high fidelity models for efficient inverse design of photonic surfaces.

Findings

Achieved spectral emissivity predictions with RMS errors < 2%

Generated multiple laser parameters sets for the same spectral target

Validated designs experimentally for energy harvesting efficiency

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.