The process of testing new solar cell technologies has traditionally been slow and costly, requiring multiple steps. Led by a fifth-year PhD student, a Johns Hopkins team has developed a machine learning method that promises to dramatically speed up this process, paving the way for more efficient and affordable renewable energy solutions.
“Our work shows that machine learning can streamline the solar cell testing process,” said team leader Kevin Lee, who worked with fellow electrical and computer engineering graduate students Arlene Chiu, Yida Lin, Sreyas Chintapalli, and Serene Kamal, and undergraduate Eric Ji, on the project. “This not only saves time and resources but opens new possibilities for clean energy technology development.”
The team’s results appear in Advanced Intelligent Systems.
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