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Machine Learning on Experimental Data for Optimizing Colloidal Quantum Dot Solar Cells
- Program: Electrical and Computer Engineering
- Course: ECE Group Undergraduate Research EN.520.516
- Year: 2022
Project Description:
In this project, we used machine learning models to assist in the characterization process of PbS colloidal quantum dot (CQD) solar cells. We performed spatially-resolved optoelectronic measurements and used experimental data to train a neural network to automatically predict several materials parameters.