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Completing density functional theory by machine learning hidden messages from molecules

Physics

Completing density functional theory by machine learning hidden messages from molecules

R. Nagai, R. Akashi, et al.

This groundbreaking research by Ryo Nagai, Ryosuke Akashi, and Osamu Sugino reveals a novel method for constructing the exchange-correlation energy functional in Kohn-Sham DFT using machine learning. Their approach surprisingly offers high accuracy across numerous molecules, on par with traditional functionals, enhancing the capabilities of DFT.

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