||Wright-Patterson AFB, OH 454337817
The next generation of optical materials will utilize wavelength-scale geometric features to manipulate light in ways beyond the capability of traditional bulk optical materials to achieve required performance. Due to the near-infinite design space for such optical metamaterials, advanced optimization strategies coupled with computational electromagnetic simulation must be employed to accelerate the design process. Machine learning approaches have demonstrated the potential to overcome the limitations of ansatz and/or iterative approaches by inverting the design process, delivering optical metamaterial designs with desired properties. We will investigate the creation of machine learning models capable of designing optical metasurfaces. These models will optimize the design of the metasurface to achieve the desired performance. Candidates with backgrounds in optics, materials science & engineering, physics, or related fields with machine learning experience are highly encouraged to contact the adviser to discuss research opportunities.
 Inverse design of broadband highly reflective metasurfaces using neural networks, Eric S. Harper, Eleanor J. Coyle, Jonathan P. Vernon, Matthew S. Mills. Phys. Rev. B, 101, 195104, 4 May 2020. DOI: 10.1103/PhysRevB.101.195104
 Artificial neural network discovery of a switchable metasurface reflector, Jonathan R. Thompson, Joshua A. Burrow, Piyush J. Shah, Jonathan Slagle, Eric S. Harper, Andre Van Rynbach, Imad Agha, Matthew S. Mills. Optics Express, 28, 17, 17 August 2020. DOI: 10.1364/OE.400360
 Particle swarm optimization of polymer-embedded broadband metasurface reflectors, Jonathan R. Thompson, Heidi D. Nelson-Quillin, Eleanor J. Coyle, Jonathan P. Vernon, Eric S. Harper, Matthew S. Mills. Optics Express, 29, 26, 20 December 2021. DOI: 10.1364/OE.444112
Machine Learning; Metasurfaces; Metamaterials; Optics; Inverse Design; Material Science & Engineering; Optimization