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Completing density functional theory by machine learning hidden messages from molecules
Physicsnpj Computational Materials

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.... show more
Introduction
Literature Review
Methodology
Key Findings
Discussion
Conclusion
Limitations
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