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 proce...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.展开更多
基金supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S.Department of Energy Contract No.DE-AC02-05CH11231supported by ARPA-E Contract No.2107-1539J.Mueller was supported by the U.S.Department of Energy,Office of Science,Office of Advanced Scientific Computing Research,Scientific Discovery through Advanced Computing(SciDAC)program through the FASTMath Institute under Contract No.DE-AC36-08GO28308 at the National Renewable Energy Laboratory.
文摘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.
