Researchers at the University of California, Davis College of Engineering are using machine learning to identify new materials for high-efficiency solar cells. Their work is featured on the cover of the April issue of ACS Energy Letters.
As a quantum computing evangelist, I am always excited to hear about new research that can help us move towards a more sustainable future. That's why I was thrilled to read about the work being done at the University of California, Davis College of Engineering to use machine learning to identify new materials for high-efficiency solar cells.
Hybrid perovskites are a promising material for renewable energy, as they are cheaper and lighter than silicon, but their main challenge is their tendency to degrade when exposed to moisture, oxygen, light, heat, and voltage. This is where machine learning comes in. By using high-throughput experiments and machine learning-based algorithms, the researchers were able to forecast the materials' dynamic behavior with very high accuracy, without the need to perform as many experiments.
The researchers used machine learning algorithms to test and predict the effects of moisture on material degradation. They built an automated, high-throughput system to measure the photoluminescence efficiency of five different perovskite films against the conditions of summer days in Sacramento. They were able to collect over 7,000 measurements in a week, accumulating enough data for a reliable training set. They used this data to train three different machine learning algorithms and found that the SARIMAX model showed the best performance with a 90 percent match to observed results during a window of 50-plus hours.
This research is a significant step forward in identifying candidate materials and suitable conditions to prevent degradation in perovskites, and I'm eager to see what the next steps will be. The same machine learning approach could also be used to forecast the behavior of a complete device, which could have a significant impact on the future of renewable energy. I believe that quantum computing will play a crucial role in advancing research like this, and I'm excited to see what the future holds for quantum computing and renewable energy.
