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Machine learning for many-body physics: efficient solution of dynamical mean-field theory
Louis-François Arsenault, O. Anatole von Lilienfeld, Andrew J. Millis
Machine learning methods for solving the equations of dynamical mean-field theory are developed. The method is demonstrated on the three dimensional Hubbard model. The key technica…
Metal-insulator transitions in GdTiO3/SrTiO3 superlattices
Se Young Park, Andrew J. Millis
The density functional plus U method is used to obtain the electronic structure, lattice relaxation and metal-insulator phase diagram of superlattices consisting of layers of G…
Density functional versus spin-density functional and the choice of correlated subspace in multi-variable effective action theories of electronic structure
Hyowon Park, Andrew J. Millis, Chris A. Marianetti
Modern extensions of density functional theory such as the density functional theory plus U and the density functional theory plus dynamical mean-field theory require choices, incl…
Density Functional plus Dynamical Mean-Field Theory of the Spin-Crossover Molecule Fe(phen)(NCS)
Jia Chen, Andrew Millis, Chris Marianetti
We study the spin-crossover molecule Fe(phen)(NCS) using density functional theory (DFT) plus dynamical mean-field theory, which allows access to observables not attainable…
Strain Control of Electronic Phase in Rare Earth Nickelates
Zhuoran He, Andrew J. Millis
We use density functional plus methods to study the effects of a tensile or compressive substrate strain on the charge-ordered insulating phase of LuNiO. The numerical resu…