For more than a century, scientists have relied on crystallography—analyzing X-ray diffraction patterns—to uncover the atomic structures of materials. This method revolutionized fields from medicine to materials science, famously enabling the discovery of DNA’s double helix.
Yet, crystallography has had a persistent flaw: it works best on large, pure crystals. When only tiny, imperfect nanocrystals are available, the method falls short, leaving the structure of countless materials unknown.
Columbia Engineering researchers used machine learning to mend these persistent issues. Their new algorithm enables the reconstruction of the atomic structure of materials from degraded diffraction patterns of fragments of nanocrystals. A feat that was previously deemed impossible has come true.
“The AI solved this problem by learning everything it could from a database of many thousands of known, but unrelated, structures,” says Simon Billinge, professor of materials science and applied physics and applied mathematics at Columbia Engineering.
“Just as ChatGPT learns the patterns of language, the AI model learned the patterns of atomic arrangements that nature allows.”
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