Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization

Journal Article · Mon Jan 13 00:00:00 EST 2025 · Engineering Applications of Artificial Intelligence

DOI:https://doi.org/10.1016/j.engappai.2024.109971· OSTI ID:2529432

Grbcic, Luka ^[1]; Park, Minok ^[1]; Müller, Juliane ^[2]; Zorba, Vassilia ^[3]; de Jong, Wibe Albert ^[1]

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1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
3. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)

Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model’s outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: 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
• OSTI ID: 2529432
• Alternate ID(s): OSTI ID: 2530908
• Report Number(s): NREL/JA--2C00-91626; ark:/13030/qt29x1g9t2
• Journal Information: Engineering Applications of Artificial Intelligence, Journal Name: Engineering Applications of Artificial Intelligence Vol. 143; ISSN 0952-1976
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Femtosecond laser processing
Inverse design
Machine learning
Photonic surfaces
Random forests
Surrogate-based optimization