seed projects

Chris Bartel, Vivian Ferry, Renee Frontiera, Russ Holmes: Machine Learning of Chirality Transfer in Circular Dichroism Spectra

Figure 1. Schematic of a chiral substrate and associated optical characterization.

The goal of this Seed project is to extract data-driven insights into the chirality transfer between plasmonic nanomaterials and achiral molecules. Circular dichroism (CD) spectra for these systems indicate tremendous heterogeneity in the magnitude, sign, and spectral features depending on the nature of the fabricated sample and location within samples being probed. The volume of spot-resolved spectra collected by CD microscopy is quickly outpacing the ability to manually interrogate these relationships. This seed project will leverage machine learning approaches to automate spectral processing and inform the preparation of new samples aimed at correlating chirality transfer with substrate properties.

Funded by the National Science Foundation through the University of Minnesota MRSEC under Award Number DMR-2011401