Automated vision speeds up material discovery and evaluation process .

A team of researchers has developed a computer vision–based method that automates the characterization of new electronic materials. Using advanced imaging—hyperspectral and RGB—the invention identifies key properties like band gap and stability with over 96% accuracy. This new approach replaces the traditionally slow, manual processes used to test materials such as semiconductors, delivering results 85 times faster. It was successfully applied to printed samples of perovskites, a promising material for next-generation solar cells.

This innovation could transform how materials are discovered and evaluated. By combining machine learning with optical imaging, the system fits into a fully automated pipeline where materials can be synthesized, analyzed, and optimized with minimal human input. Its creators aim to support faster progress in areas like solar energy, electronics, and sustainable materials by enabling laboratories to run 24/7, accelerating experimentation, reducing bottlenecks, and shortening the path from idea to real-world application.