Mar 5, 2019

Scientists use machine learning to identify high-performing solar materials

Posted by in categories: engineering, robotics/AI, solar power, supercomputing, sustainability

Finding the best light-harvesting chemicals for use in solar cells can feel like searching for a needle in a haystack. Over the years, researchers have developed and tested thousands of different dyes and pigments to see how they absorb sunlight and convert it to electricity. Sorting through all of them requires an innovative approach.

Now, thanks to a study that combines the power of supercomputing with and experimental methods, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Cambridge in England have developed a novel “design to device” approach to identify promising materials for dye-sensitized solar cells (DSSCs). DSSCs can be manufactured with low-cost, scalable techniques, allowing them to reach competitive performance-to-price ratios.

The team, led by Argonne materials scientist Jacqueline Cole, who is also head of the Molecular Engineering group at the University of Cambridge’s Cavendish Laboratory, used the Theta supercomputer at the Argonne Leadership Computing Facility (ALCF) to pinpoint five high-performing, low-cost dye materials from a pool of nearly 10,000 candidates for fabrication and device testing. The ALCF is a DOE Office of Science User Facility.

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