Tobias Kramer, ZIB

"What is... efficiently solving hierarchical equations of motion?"

The hierarchical equations of motions provide an exact solution for open quantum system dynamics, also for larger systems. An important application is the description of energy transfer in photosynthetic systems from the antenna to the reaction center and the computation of the corresponding time-resolved spectra. The methods captures non-Markovian effects and strong system-environment interactions, which affect the thermalization and decoherence process. On parallel and distributed computers we provide an efficient implementation of the method [1], which is also available as GPU cloud computing tool at nanohub.org [2]. The obtained data is efficiently compressed by using neural networks and machine learning [3].

Reference:
[1] T. Kramer, M. Noack, A. Reinefeld, M. Rodriguez, Y. Zelinskyy: Efficient calculation of open quantum system dynamics and time-resolved spectroscopy with Distributed Memory HEOM (DM-HEOM); Journal of Computational Chemistry (2018),
https://doi.org/10.1002/jcc.25354
[2] nanoHUB.org: Exciton Dynamics Lab for Light-Harvesting Complexes (GPU-HEOM)
https://nanohub.org/resources/gpuheompop/usage
[3] M. Rodriguez, T. Kramer: Machine Learning of Two-Dimensional Spectroscopic Data
https://arxiv.org/abs/1810.01124

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