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The process of testing new solar cell technologies has traditionally been slow and costly, requiring multiple steps. Led by a fifth-year Ph.D. 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.

A major hurdle in commercializing new solar materials and devices is the lengthy fabrication-testing-iteration cycle. Optimizing a new solar cell material for the market is an arduous process. After a device is made, multiple time-consuming measurements are needed to understand its material properties. This data is then used to adjust the fabrication process, repeating the cycle.

The new method drastically reduces this time by extracting all the materials' important characteristics from a single measurement. Unlike other methods trained on computer-simulated data—which often produce inaccurate results—the Hopkins team's approach uses real-world data.

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