The sun continuously transmits trillions of watts of energy to the Earth. It will be doing so for billions more years.
Yet, we have only just begun tapping into that abundant, renewable source of energy at affordable cost.
Machine learning methods are being developed at Argonne to advance solar energy research with perovskites. In the days of Thomas Edison, scientists discovered new materials by the laborious process of trial and error with many different candidates until one works. Over the last several decades, they have also relied on labor-intensive calculations requiring as long as a thousand hours to predict a material’s properties. Now, they can shortcut both discovery processes by calling upon machine learning.
“Unlike silicon or cadmium telluride, the possible variations of halides combined with perovskites are essentially unlimited,” said Chan. “There is thus an urgent need to develop a method that can narrow the promising candidates to a manageable number. To that end, machine learning is a perfect tool.”
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