Researchers at Korea University have developed a machine learning model for predicting sheet resistance in phosphorus oxychloride (POCl3) doping processes in solar cell manufacturing. “Our study aims ...

Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. Using ...

How can machine learning help determine the best times and ways to use solar energy? This is what a recent study published in Advances in Atmospheric Sciences hopes to address as a team of researchers ...

Solar-collecting windows could make office buildings and skyscrapers more energy efficient, but harnessing solar power while retaining transparency is a tricky engineering problem. A new study from ...

Perovskites are a class of materials with great potential as solar cells. UC Davis materials scientists have used machine learning to explore the wide variety of perovskite formulas to find those best ...

The number of solar field construction projects is expected to rise dramatically as McKinsey projects United States solar capacity to explode from 73 gigawatts in 2021 to 617 gigawatts in 2032.

A solar expert got many talking when he showed the electric lifter he used to install a bifacial panel on a customer's roof.