Machine Learning on Experimental Data for Optimizing Colloidal Quantum Dot Solar Cells
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.
