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For glass scientists, the periodic table is their oyster—virtually all elements turn into a glass if quenched fast enough. Yet with so many options, finding “pearls” (optimal glass compositions) among the essentially limitless possibilities is extremely difficult when relying on trial-and-error experimental approaches.

Numerous recent studies use machine learning to predict glass composition. What these studies collectively teach us, and what they offer to future research, is the subject of a new open-access review paper by researchers at the University of California, Los Angeles.

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