Machine-learning-driven accelerated design-method for meta-devices

Sumbel Ijaz, Sadia Noureen, Bacha Rehman, Osamah Aldaghri, Humberto Cabrera, Khalid H. Ibnaouf, Nawal Madkhali, Muhammad Qasim Mehmood

Research output: Contribution to journalArticlepeer-review


Metasurface-based solar absorbers are widely acknowledged in green energy applications. The traditional roadmap for designing meta-absorbers relies on hefty trial-and-error computations for reaching design goals. In contrast, emerging machine-learning (ML) techniques can make such designs faster and more efficient, while conserving computational resources. ML models, i.e. Decision Tree (DT) and Random Forest (RF) Regressors are demonstrated in this work to design a variety of meta-absorbers. The models have been trained to generate desired electromagnetic spectrum, shapes, and geometries of meta-atoms during forward and inverse configurations, respectively. The MSE of DT and RF are 8.61 × 10−10 and 1.56 × 10−2 for forward, while 4.42 × 10−2 and 1.35 × 10−1 for inverse models, respectively.
Original languageEnglish
Pages (from-to)106951
Number of pages1
JournalMaterials Today Communications
Publication statusPublished - 1 Sept 2023

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