Aktivität: Vorträge › Vortrag › Science to Science
Beschreibung
The RPA-OEP method allows us to construct local exchange-correlation potentials corresponding to the random-phase approximation (RPA) energy functional. We develop a machine learning (ML) approach that short-cuts the RPA-OEP equation and maps the RPA to a pure density functional. The ingredients for the ML-RPA energy density are only averages of the electronic density and its gradient in some real-space environment. That is, our ML-RPA functionals can be considered as non-local extensions to the usual gradient approximations. The exchange-correlation potentials provided from RPA-OEP reference calculations serve as derivative information for the ML fit. This greatly enhances the data set size contrast to common approaches using only energies for fitting. In this talk, we will present our ML-RPA framework from a theoretical and technical perspective and show practical applications to real systems.