Topological nanophotonics and artificial neural networks

Area of Science:

• Condensed matter physics
• Quantum mechanics
• Materials science

Background:

• Photonic topological insulators exhibit unique edge states crucial for robust light manipulation.
• Designing these materials often involves complex inverse problems.
• Existing methods struggle to efficiently identify parameters for desired topological properties.

Purpose of the Study:

• To introduce artificial neural networks (ANNs) for designing and characterizing photonic topological insulators.
• To demonstrate the capability of ANNs in solving inverse design problems for topological nanophotonics.
• To enable the identification of specific parameters for achieving protected edge states at target frequencies.

Main Methods:

• Application of artificial neural networks (ANNs) to analyze band structures.
• Utilizing machine learning to identify design parameters for photonic topological insulators.
• Investigating various systems including 1D photonic crystals, [Formula: see text]-symmetric chains, and cylindrical structures.

Main Results:

• ANNs successfully identified parameters for complex topological insulators.
• Protected edge states were achieved at target frequencies using the machine learning approach.
• The study highlights ANNs as a powerful tool for inverse problems in nanophotonics.

Conclusions:

• Artificial neural networks offer an efficient solution for the inverse design of photonic topological insulators.
• This approach accelerates the discovery and characterization of novel topological materials.
• The findings pave the way for advanced applications in topological nanophotonics.