  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.

Optics;Applied Physics