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Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements, but many systems struggle when real-world data do not match ideal conditions. Measurements collected from different instruments, experiments or simulations often vary widely in resolution, noise and reliability. Traditional machine-learning models typically assume those differences are negligible — an assumption that can limit accuracy and trustworthiness.

Pairing elements such as sulfur, selenium or tellurium with metals produces compounds whose atomic interactions give them unusual and useful electrical, optical and magnetic behavior. These materials, called chalcogenides, are the focus of Qihua “David” Zhang’s work as a postdoctoral researcher in the Two-Dimensional Crystal Consortium (2DCC) at Penn State in the laboratory of Stephanie Law, Wilson Family Fellow and associate professor of materials science and engineering.