Optical engineering of carbon-based nanowires using machine learning

Graphene is a 2D material which shows many promising applications in diverse technologies such as sensors, field effect transistors, and solar cells. Graphene can be formed into quasi-1D nanoribbons which show further device application. The optoelectronic properties of graphene nanoribbons are readily manipulated through multiple approaches. We demonstrate engineering of the optical response of graphene nanoribbons using density functional theory to compute bandgaps and dielectric functions and machine learning. Structure, width, strain, electronic doping, edge functionalization, and point-substitutions are included as methods to control the optoelectronic properties of nanoribbons. Nanoribbon structures are procedurally generated and evaluated for electronic bandgap and dielectric function. Machine learning is used to identify trends between nanoribbon structure and optical properties and predict new structures with desired optical response.