MIT EECS | Analog Devices Undergraduate Research and Innovation Scholar
Unraveling the Spectral Correlations for 2D Materials
Electrical Engineering and Computer Science
- Materials Science
Photoluminescence is an essential property for optoelectronic and photonic applications. Notably, two-dimensional (2D) transitional metal dichalcogenides (TMDCs) exhibit intense and tunable PL as their thickness is reduced to monolayers. Among the prevalent characterization tools for 2D materials, Raman spectroscopy stands out as a fast and non-destructive technique capable of probing material crystallinities and perturbations such as doping and strain. The general aim of this project is to develop machine learning techniques that can learn about the structure of 2D materials from simpler spectroscopic experiments like Raman spectroscopy for tungsten diselenide (WSe2), and predict the results of more complicated experiments without having to perform them.
This SuperUROP will help me explore the intersection of machine learning and physics. I have taken machine learning courses at MIT but haven’t had the chance to apply them in a research, and this field combined with the fundamental physics is pretty interesting to me. I also see this as an opportunity to decide where I would like to do my MEng, and whether I would like to pursue grad school in this field or not.