Machine learning the derivative discontinuity of density-functional theory

Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned functionals are trained for systems with an integer number of particles. As such, they are unable to reproduce some crucial...

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Veröffentlicht in:	Machine learning: science and technology 2022-03, Vol.3 (1), p.15011
Hauptverfasser:	Gedeon, Johannes, Schmidt, Jonathan, Hodgson, Matthew J P, Wetherell, Jack, Benavides-Riveros, Carlos L, Marques, Miguel A L
Format:	Artikel
Sprache:	eng
Schlagworte:	Density functional theory Discontinuity electronic structure ensemble density functional theory Integers Machine learning Neural networks quantum physics Training
Online-Zugang:	Volltext
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