The engineers in Iowa State are developing machine learning tools in order to quickly and cheaply design better solar cells. They need to use machine learning to scan swiftly existing data, learn patterns, make predictions, and help them immediately reach their design goal, in spite of using traditional and costly methods. Baskar Ganapathysubramanian at Iowa State University said, “The project’s immediate goal is to develop machine-learning theory and software tools that will allow rapid identification of organic thin-film structures that enhance solar cell performance and are easy to manufacture. The broader goal is to demonstrate that machine learning can help rapidly design all kinds of technologies”.
An Iowa State associate professor of mechanical engineering, Soumik Sarkar said, “We’re looking at a non-traditional way of doing machine learning – we’re doing science with machine learning. Machine learning has been used to make your next Netflix recommendation. The new frontier is trying to see if machine learning can help engineers or scientists do engineering or science better”. It is noteworthy that engineers better knew the machine learning process could generalize design-for-manufacturing rules to identify difficult-to-manufacture features in a complex part. It helps to accelerate the design process and allows scientists to identify manufacturing bottlenecks at the design level.
Ganapathysubramanian also said, “While better solar cells are certainly a good thing, but that’s not the best thing that could come from this project. The most important outcomes are going to be theory and software tools that allow us to design new technologies in a fast and agile manner. That’s the key outcome that ARPA-E expects”. A recent grant of more than 2 million U.S dollars over 2 years from the ARPA-E (Advanced Research Projects Agency-Energy) of the U.S Department of Energy will support their exploration of that idea. The Iowa State-led project is one of 23 supported by more than 15 million U.S dollars from the research agency’s “Differentiate” program. It was dedicated to accelerating the search for energy innovations.