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ESROP Physics-Informed Neural Network Engine for Perovskite Solar Cells
Perovskite solar cells are a promising new solar technology that could enable cheaper and more efficient renewable energy. However, their performance can vary widely because even small changes during manufacturing can strongly affect how well the devices work. This project uses advanced artificial intelligence techniques, combined with known physical principles, to analyze a large collection of solar cell performance data. By identifying patterns in how different devices behave and tailoring models to these patterns, the project aims to create a more reliable way to predict and understand perovskite solar cell performance. Ultimately, this work will help support the development of more consistent, scalable, and commercially viable solar energy technologies.
Faculty Supervisor:
Arthur Chan
Student:
Partner:
National University of Singapore
Discipline:
Engineering
Sector:
Education
University:
University of Toronto
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.