Using machine learning to find reliable and low-cost solar cells
Researchers at the University of California, Davis College of Engineering are using machine learning to identify new materials for high-efficiency solar cells. Using high-throughput experiments and machine learning-based algorithms, they have found it is possible to forecast the materials’ dynamic behavior with very high accuracy, without the need to perform as many experiments.
The work is featured on the cover of the April issue of ACS Energy Letters.
Hybrid perovskites are organic-inorganic molecules that have received a lot of attention ...










