Memory truncated Kadanoff-Baym equations

Stahl C, Dasari NR, Li J, Picano A, Werner P, Eckstein M (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Physical Review B American Physical Society

Book Volume: 105

Article Number: 115146

Journal Issue: 11

DOI: 10.1103/PhysRevB.105.115146

Abstract

The Keldysh formalism for nonequilibrium Green's functions is a powerful theoretical framework for the description of the electronic structure, spectroscopy, and dynamics of strongly correlated systems. However, the underlying Kadanoff-Baym equations (KBE) for the two-time Keldysh Green's functions involve a memory kernel, which results in a high computational cost for long simulation times tmax, with a cubic scaling of the computation time with tmax. Truncation of the memory kernel can reduce the computational cost to linear scaling with tmax, but the required memory times will depend on the model and the diagrammatic approximation to the self-energy. We explain how a truncation of the memory kernel can be incorporated into the time-propagation algorithm to solve the KBE, and investigate the systematic truncation of the memory kernel for the Hubbard model in different parameter regimes, and for different diagrammatic approximations. The truncation is easier to control within dynamical mean-field solutions, where it is applied to a momentum-independent self-energy. Here, simulation times up to two orders of magnitude longer are accessible both in the weak and strong coupling regime, allowing for a study of long-time phenomena such as the crossover between prethermalization and thermalization dynamics.

Authors with CRIS profile

Christopher Stahl Lehrstuhl für Theoretische Festkörperphysik Nagamalleswara Rao Dasari Lehrstuhl für Theoretische Festkörperphysik Antonio Picano Lehrstuhl für Theoretische Festkörperphysik Martin Eckstein Lehrstuhl für Theoretische Festkörperphysik

Involved external institutions

Paul Scherrer Institute (PSI) CH Switzerland (CH) University of Fribourg / Universitè de Fribourg CH Switzerland (CH)

How to cite

APA:

Stahl, C., Dasari, N.R., Li, J., Picano, A., Werner, P., & Eckstein, M. (2022). Memory truncated Kadanoff-Baym equations. Physical Review B, 105(11). https://doi.org/10.1103/PhysRevB.105.115146

MLA:

Stahl, Christopher, et al. "Memory truncated Kadanoff-Baym equations." Physical Review B 105.11 (2022).

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