The Cosmos Podcast
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The Cosmos Podcast
Machine learning and solar energy
As we build a clean energy future, solar energy research is diversifying. This is one of the focuses of the ARC Centre of Excellence for Exciton Science, a collaboration between Australian unis and industry that explores how light interacts with advanced materials. In particular, they study Excitons, an excited state of matter that is crucial to semiconductors, which are used in light-based applications from lasers to solar cells. One group within the ARC is working on building better solar panels. Right now, typical silicon solar cells only capture about 30% of the energy that lands on them. So part of this group's research is trying to identify cheaper and more efficient materials to make these cells. Today we talk to Carl Belle, the lead researcher with a team at RMIT University and the ARC Centre of Excellence in Exciton Science in Melbourne. He is trying to find new candidate materials with these cells using an interesting approach: machine learning. Today’s interview is hosted by Cosmos journalist Lauren Fuge.
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