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Perovskite solar cells need to learn resilience

Researchers have shown that machine learnng can identify hybrid pervoskites that are both high-performing and resiliant to environmental conditions.

United statesAbigail heringMarina leiteMeghna srivastavaEnergy lettersUniversity of californiaDavi college

Environmental News Network - Using Machine Learning to Find Reliable and Low-Cost Solar Cells

Environmental News Network - Using Machine Learning to Find Reliable and Low-Cost Solar Cells
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United statesFernando tomMarina leiteWikimedia commonsEnergy lettersDavis college of engineeringUniversity of californiaDavis college

Using machine learning to find reliable and low-cost solar cells

Using machine learning to find reliable and low-cost solar cells
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Sandia national laboratoriesUnited statesJuan pablo correa baenaMarina leiteMeghna srivastavaAbigail heringNational science foundationEnergy lettersScience foundationSandia national

Using machine learning to find reliable and l

Hybrid perovskites are organic-inorganic molecules that have received a lot of attention over the past 10 years for their potential use in renewable energy. Some are comparable in efficiency to silicon for making solar cells, but they are cheaper to make and lighter, potentially allowing a wide range of applications, including light-emitting devices. However, they tend to degrade way more readily than silicon when exposed to moisture, oxygen, light, heat, and voltage. UC Davis researchers used machine learning and high-throughput experiments to identify perovskites with optimal qualities out of the very large field of possible structures.

United statesSandia national laboratoriesAbigail heringJuan pablo correa baenaMeghna srivastavaMarina leiteMethod of researchUniversity of californiaEnergy lettersNational science foundationDavis college of engineeringDavis collegeScience foundationSandia nationalLearning enables predictionHalide perovskite optical behavior