Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency

TL;DR

This paper reviews how machine learning and optimization algorithms can be used to design graphene metamaterials with enhanced plasmon-induced transparency, demonstrating effective inverse design and performance optimization methods.

Contribution

It introduces data-driven approaches using classical machine learning algorithms for the inverse design and optimization of graphene metamaterials, highlighting the effectiveness of random forest in this context.

Findings

Random forest outperforms other algorithms in accuracy and speed.

Optimized spectra show a maximum difference of 0.97 between peaks and dips.

The methods provide guidance for intelligent photonic device design.

Abstract

Machine learning and optimization algorithms have been widely applied in the design and optimization for photonic devices. In this article, we briefly review recent progress of this field of research and show some data-driven applications (e.g. spectrum prediction, inverse design and performance optimization) for novel graphene metamaterials (GMs). The structure of the GMs is well-designed to achieve the wideband plasmon induced transparency effect, which is regarded as optimization object and can be theoretically demonstrated by using transfer matrix method. Some classical machine learning algorithms, including k nearest neighbour, decision tree, random forest and artificial neural networks, are utilized to equivalently substitute the numerical simulation in the forward spectrum prediction and complete the inverse design for the GMs. The calculated results demonstrate that all the algorithms are effective and the random forest has advantages in terms of accuracy and training speed. Moreover, the single-objective and multi-objective optimization algorithms are used to achieve steep transmission characteristics by synthetically taking many performance metrics into consideration. The maximum difference between the transmission peaks and dips in the optimized transmission spectrum can reach 0.97. In comparison to previous works, we provide a guidance for intelligent design of photonic devices and advanced materials based on machine learning and evolutionary algorithms.