MIT researchers have developed a computer vision method that accelerates the screening of electronic materials. This technique automates the analysis of images of printed semiconducting samples, estimating key properties like band gap and stability 85 times faster than traditional methods. It uses hyperspectral and RGB imaging combined with machine learning algorithms to assess materials’ electronic properties quickly and accurately. This advancement could expedite the discovery of new materials for solar cells and other applications, integrating into an autonomous materials screening system for continuous, automated analysis.
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