Researchers In US & China Use Machine Learning To Make Better Solar Panels

Solar power and advanced computing are a key cleantech intersection point. From renewables return on investment optimization to optimal rooftop commercial solar deployment, machine learning is helping us get more efficient and effective in our global transformation. But it’s deeper than that. Researchers in the US and China are using machine learning to discover new solar panel chemistries to increase the base efficiency and economic effectiveness of solar panels. They are trialing hundreds or thousands of combinations in virtual test beds before bringing them into the physical world, a key element of the machine-to-reality value proposition. Let’s start in the United States with Jinxin Li, Basudev Pradhan, Surya Gaur, and Jayan Thomas from the sun-drenched campus of the University of Central Florida. Their focus is on perovskite solar panels.