Driving enhanced exciton transfer by automatic differentiation
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 syst...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/add23b |
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| author | E Ballarin D A Chisholm A Smirne M Paternostro F Anselmi S Donadi |
| author_facet | E Ballarin D A Chisholm A Smirne M Paternostro F Anselmi S Donadi |
| author_sort | E Ballarin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4c8ba76195ec4a59bfe3414fae3edcef |
| institution | DOAJ |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-4c8ba76195ec4a59bfe3414fae3edcef2025-08-20T03:10:10ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016202503410.1088/2632-2153/add23bDriving enhanced exciton transfer by automatic differentiationE Ballarin0https://orcid.org/0000-0003-3673-0665D A Chisholm1A Smirne2https://orcid.org/0000-0003-4698-9304M Paternostro3F Anselmi4https://orcid.org/0000-0002-0264-4761S Donadi5https://orcid.org/0000-0001-6290-5065Department of Mathematics, Informatics, and Geoscience, University of Trieste , via Alfonso Valerio 2, 34127 Trieste, ItalyUniversità degli Studi di Palermo , Dipartimento di Fisica e Chimica—Emilio Segrè, via Archirafi 36, 90123 Palermo, ItalyDipartimento di Fisica ‘Aldo Pontremoli’, Università degli Studi di Milano , via Celoria 16, 20133 Milan, Italy; Istituto Nazionale di Fisica Nucleare , Sezione di Milano,via Celoria 16, 20133 Milan, ItalyUniversità degli Studi di Palermo , Dipartimento di Fisica e Chimica—Emilio Segrè, via Archirafi 36, 90123 Palermo, Italy; Centre for Quantum Materials and Technologies , School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, United KingdomDepartment of Mathematics, Informatics, and Geoscience, University of Trieste , via Alfonso Valerio 2, 34127 Trieste, Italy; MIT, 77 Massachusetts Ave, Cambridge , MA 02139, United States of AmericaCentre for Quantum Materials and Technologies , School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, United Kingdom; Istituto Nazionale di Fisica Nucleare , Sezione di Trieste, via Alfonso Valerio 2, 34127 Trieste, ItalyWe 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.https://doi.org/10.1088/2632-2153/add23bexciton transferdriving optimisationautomatic differentiationmachine learning |
| spellingShingle | E Ballarin D A Chisholm A Smirne M Paternostro F Anselmi S Donadi Driving enhanced exciton transfer by automatic differentiation Machine Learning: Science and Technology exciton transfer driving optimisation automatic differentiation machine learning |
| title | Driving enhanced exciton transfer by automatic differentiation |
| title_full | Driving enhanced exciton transfer by automatic differentiation |
| title_fullStr | Driving enhanced exciton transfer by automatic differentiation |
| title_full_unstemmed | Driving enhanced exciton transfer by automatic differentiation |
| title_short | Driving enhanced exciton transfer by automatic differentiation |
| title_sort | driving enhanced exciton transfer by automatic differentiation |
| topic | exciton transfer driving optimisation automatic differentiation machine learning |
| url | https://doi.org/10.1088/2632-2153/add23b |
| work_keys_str_mv | AT eballarin drivingenhancedexcitontransferbyautomaticdifferentiation AT dachisholm drivingenhancedexcitontransferbyautomaticdifferentiation AT asmirne drivingenhancedexcitontransferbyautomaticdifferentiation AT mpaternostro drivingenhancedexcitontransferbyautomaticdifferentiation AT fanselmi drivingenhancedexcitontransferbyautomaticdifferentiation AT sdonadi drivingenhancedexcitontransferbyautomaticdifferentiation |