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Driving enhanced exciton transfer by automatic differentiation

E Ballarin
•
D A Chisholm
•
A Smirne
altro
S Donadi
2025
  • journal article

Periodico
MACHINE LEARNING: SCIENCE AND TECHNOLOGY
Abstract
We model and study the processes of excitation, absorption, and transfer in various networks. The model consists of a harmonic oscillator representing a single-mode radiation field, a two-level system acting as an antenna, a network through which the excitation propagates, and another two-level system at the end serving as a sink. We investigate how off-resonant excitations can be optimally absorbed and transmitted through the network. Three strategies are considered: optimising network energies, adjusting the couplings between the radiation field, the antenna, and the network, or introducing and optimising driving fields at the start and end of the network. These strategies are tested on three different types of network with increasing complexity: nearest-neighbour and star configurations, and one associated with the Fenna-Matthews-Olson complex. The results show that, among the various strategies, the introduction of driving fields is the most effective, leading to a significant increase in the probability of reaching the sink in a given time. This result remains stable across networks of varying dimensionalities and types, and the driving process requires only a few parameters to be effective.
DOI
10.1088/2632-2153/add23b
WOS
WOS:001487318500001
Archivio
https://hdl.handle.net/11368/3117664
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-105005283321
https://iopscience.iop.org/article/10.1088/2632-2153/add23b
https://ricerca.unityfvg.it/handle/11368/3117664
Diritti
open access
license:creative commons
license uri:http://creativecommons.org/licenses/by/4.0/
FVG url
https://arts.units.it/bitstream/11368/3117664/1/Ballarin_2025_Mach._Learn.__Sci._Technol._6_025034.pdf
Soggetti
  • automatic differentia...

  • driving optimisation

  • exciton transfer

  • machine learning

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