---
_id: '12150'
abstract:
- lang: eng
text: Methods inspired from machine learning have recently attracted great interest
in the computational study of quantum many-particle systems. So far, however,
it has proven challenging to deal with microscopic models in which the total number
of particles is not conserved. To address this issue, we propose a variant of
neural network states, which we term neural coherent states. Taking the Fröhlich
impurity model as a case study, we show that neural coherent states can learn
the ground state of nonadditive systems very well. In particular, we recover exact
diagonalization in all regimes tested and observe substantial improvement over
the standard coherent state estimates in the most challenging intermediate-coupling
regime. Our approach is generic and does not assume specific details of the system,
suggesting wide applications.
acknowledgement: 'We acknowledge fruitful discussions with G. Bighin, G. Fabiani,
A. Ghazaryan, C. Lampert, and A. Volosniev at various stages of this work. W.R.
acknowledges support through a DOC Fellowship of the Austrian Academy of Sciences
and has received funding from the EU Horizon 2020 programme under the Marie Skłodowska-Curie
Grant Agreement No. 665385. M.L. and J.H.M. acknowledge support by the European
Research Council (ERC) Starting Grant No. 801770 (ANGULON) and Synergy Grant No.
856538 (3D-MAGiC), respectively. This work is part of the Shell-NWO/FOMinitiative
“Computational sciences for energy research” of Shell and Chemical Sciences, Earth
and Life Sciences, Physical Sciences, FOM and STW. '
article_number: '155127'
article_processing_charge: No
article_type: original
author:
- first_name: Wojciech
full_name: Rzadkowski, Wojciech
id: 48C55298-F248-11E8-B48F-1D18A9856A87
last_name: Rzadkowski
orcid: 0000-0002-1106-4419
- first_name: Mikhail
full_name: Lemeshko, Mikhail
id: 37CB05FA-F248-11E8-B48F-1D18A9856A87
last_name: Lemeshko
orcid: 0000-0002-6990-7802
- first_name: Johan H.
full_name: Mentink, Johan H.
last_name: Mentink
citation:
ama: Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for
nonadditive systems. Physical Review B. 2022;106(15). doi:10.1103/physrevb.106.155127
apa: Rzadkowski, W., Lemeshko, M., & Mentink, J. H. (2022). Artificial neural
network states for nonadditive systems. Physical Review B. American Physical
Society. https://doi.org/10.1103/physrevb.106.155127
chicago: Rzadkowski, Wojciech, Mikhail Lemeshko, and Johan H. Mentink. “Artificial
Neural Network States for Nonadditive Systems.” Physical Review B. American
Physical Society, 2022. https://doi.org/10.1103/physrevb.106.155127.
ieee: W. Rzadkowski, M. Lemeshko, and J. H. Mentink, “Artificial neural network
states for nonadditive systems,” Physical Review B, vol. 106, no. 15. American
Physical Society, 2022.
ista: Rzadkowski W, Lemeshko M, Mentink JH. 2022. Artificial neural network states
for nonadditive systems. Physical Review B. 106(15), 155127.
mla: Rzadkowski, Wojciech, et al. “Artificial Neural Network States for Nonadditive
Systems.” Physical Review B, vol. 106, no. 15, 155127, American Physical
Society, 2022, doi:10.1103/physrevb.106.155127.
short: W. Rzadkowski, M. Lemeshko, J.H. Mentink, Physical Review B 106 (2022).
date_created: 2023-01-12T12:07:49Z
date_published: 2022-10-15T00:00:00Z
date_updated: 2023-08-04T09:01:48Z
day: '15'
department:
- _id: MiLe
doi: 10.1103/physrevb.106.155127
ec_funded: 1
external_id:
arxiv:
- '2105.15193'
isi:
- '000875189100005'
intvolume: ' 106'
isi: 1
issue: '15'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2105.15193'
month: '10'
oa: 1
oa_version: Preprint
project:
- _id: 05A235A0-7A3F-11EA-A408-12923DDC885E
grant_number: '25681'
name: Analytic and machine learning approaches to composite quantum impurities
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 2688CF98-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '801770'
name: 'Angulon: physics and applications of a new quasiparticle'
publication: Physical Review B
publication_identifier:
eissn:
- 2469-9969
issn:
- 2469-9950
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Artificial neural network states for nonadditive systems
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 106
year: '2022'
...
---
_id: '10759'
abstract:
- lang: eng
text: In this Thesis, I study composite quantum impurities with variational techniques,
both inspired by machine learning as well as fully analytic. I supplement this
with exploration of other applications of machine learning, in particular artificial
neural networks, in many-body physics. In Chapters 3 and 4, I study quasiparticle
systems with variational approach. I derive a Hamiltonian describing the angulon
quasiparticle in the presence of a magnetic field. I apply analytic variational
treatment to this Hamiltonian. Then, I introduce a variational approach for non-additive
systems, based on artificial neural networks. I exemplify this approach on the
example of the polaron quasiparticle (Fröhlich Hamiltonian). In Chapter 5, I continue
using artificial neural networks, albeit in a different setting. I apply artificial
neural networks to detect phases from snapshots of two types physical systems.
Namely, I study Monte Carlo snapshots of multilayer classical spin models as well
as molecular dynamics maps of colloidal systems. The main type of networks that
I use here are convolutional neural networks, known for their applicability to
image data.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Wojciech
full_name: Rzadkowski, Wojciech
id: 48C55298-F248-11E8-B48F-1D18A9856A87
last_name: Rzadkowski
orcid: 0000-0002-1106-4419
citation:
ama: Rzadkowski W. Analytic and machine learning approaches to composite quantum
impurities. 2022. doi:10.15479/at:ista:10759
apa: Rzadkowski, W. (2022). Analytic and machine learning approaches to composite
quantum impurities. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10759
chicago: Rzadkowski, Wojciech. “Analytic and Machine Learning Approaches to Composite
Quantum Impurities.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10759.
ieee: W. Rzadkowski, “Analytic and machine learning approaches to composite quantum
impurities,” Institute of Science and Technology Austria, 2022.
ista: Rzadkowski W. 2022. Analytic and machine learning approaches to composite
quantum impurities. Institute of Science and Technology Austria.
mla: Rzadkowski, Wojciech. Analytic and Machine Learning Approaches to Composite
Quantum Impurities. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10759.
short: W. Rzadkowski, Analytic and Machine Learning Approaches to Composite Quantum
Impurities, Institute of Science and Technology Austria, 2022.
date_created: 2022-02-16T13:27:37Z
date_published: 2022-02-21T00:00:00Z
date_updated: 2024-02-28T13:01:59Z
day: '21'
ddc:
- '530'
degree_awarded: PhD
department:
- _id: GradSch
- _id: MiLe
doi: 10.15479/at:ista:10759
ec_funded: 1
file:
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checksum: 0fc54ad1eaede879c665ac9b53c93e22
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date_created: 2022-02-21T13:58:16Z
date_updated: 2022-02-22T07:20:12Z
file_id: '10785'
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file_size: 17668233
relation: source_file
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checksum: 22d2d7af37ca31f6b1730c26cac7bced
content_type: application/pdf
creator: wrzadkow
date_created: 2022-02-21T14:02:54Z
date_updated: 2022-02-21T14:02:54Z
file_id: '10786'
file_name: Rzadkowski_thesis_final.pdf
file_size: 13307331
relation: main_file
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file_date_updated: 2022-02-22T07:20:12Z
has_accepted_license: '1'
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: '120'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '10762'
relation: part_of_dissertation
status: public
- id: '8644'
relation: part_of_dissertation
status: public
- id: '7956'
relation: part_of_dissertation
status: public
- id: '415'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Mikhail
full_name: Lemeshko, Mikhail
id: 37CB05FA-F248-11E8-B48F-1D18A9856A87
last_name: Lemeshko
orcid: 0000-0002-6990-7802
title: Analytic and machine learning approaches to composite quantum impurities
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2022'
...
---
_id: '10762'
abstract:
- lang: eng
text: Methods inspired from machine learning have recently attracted great interest
in the computational study of quantum many-particle systems. So far, however,
it has proven challenging to deal with microscopic models in which the total number
of particles is not conserved. To address this issue, we propose a new variant
of neural network states, which we term neural coherent states. Taking the Fröhlich
impurity model as a case study, we show that neural coherent states can learn
the ground state of non-additive systems very well. In particular, we observe
substantial improvement over the standard coherent state estimates in the most
challenging intermediate coupling regime. Our approach is generic and does not
assume specific details of the system, suggesting wide applications.
acknowledgement: "We acknowledge fruitful discussions with Giacomo Bighin, Giammarco
Fabiani, Areg Ghazaryan, Christoph\r\nLampert, and Artem Volosniev at various stages
of this work. W.R. is a recipient of a DOC Fellowship of the\r\nAustrian Academy
of Sciences and has received funding from the EU Horizon 2020 programme under the
Marie\r\nSkłodowska-Curie Grant Agreement No. 665385. M. L. acknowledges support
by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). This
work is part of the Shell-NWO/FOM-initiative “Computational sciences for energy
research” of Shell and Chemical Sciences, Earth and Life Sciences, Physical Sciences,
FOM and STW."
article_processing_charge: No
author:
- first_name: Wojciech
full_name: Rzadkowski, Wojciech
id: 48C55298-F248-11E8-B48F-1D18A9856A87
last_name: Rzadkowski
orcid: 0000-0002-1106-4419
- first_name: Mikhail
full_name: Lemeshko, Mikhail
id: 37CB05FA-F248-11E8-B48F-1D18A9856A87
last_name: Lemeshko
orcid: 0000-0002-6990-7802
- first_name: Johan H.
full_name: Mentink, Johan H.
last_name: Mentink
citation:
ama: Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for
non-additive systems. arXiv. doi:10.48550/arXiv.2105.15193
apa: Rzadkowski, W., Lemeshko, M., & Mentink, J. H. (n.d.). Artificial neural
network states for non-additive systems. arXiv. https://doi.org/10.48550/arXiv.2105.15193
chicago: Rzadkowski, Wojciech, Mikhail Lemeshko, and Johan H. Mentink. “Artificial
Neural Network States for Non-Additive Systems.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2105.15193.
ieee: W. Rzadkowski, M. Lemeshko, and J. H. Mentink, “Artificial neural network
states for non-additive systems,” arXiv. .
ista: Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for
non-additive systems. arXiv, 10.48550/arXiv.2105.15193.
mla: Rzadkowski, Wojciech, et al. “Artificial Neural Network States for Non-Additive
Systems.” ArXiv, doi:10.48550/arXiv.2105.15193.
short: W. Rzadkowski, M. Lemeshko, J.H. Mentink, ArXiv (n.d.).
date_created: 2022-02-17T11:18:57Z
date_published: 2021-05-31T00:00:00Z
date_updated: 2023-09-07T13:44:16Z
day: '31'
department:
- _id: MiLe
doi: 10.48550/arXiv.2105.15193
ec_funded: 1
external_id:
arxiv:
- '2105.15193'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2105.15193
month: '05'
oa: 1
oa_version: Preprint
page: '2105.15193'
project:
- _id: 2688CF98-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '801770'
name: 'Angulon: physics and applications of a new quasiparticle'
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: arXiv
publication_status: submitted
related_material:
record:
- id: '10759'
relation: dissertation_contains
status: public
status: public
title: Artificial neural network states for non-additive systems
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '8644'
abstract:
- lang: eng
text: Determining the phase diagram of systems consisting of smaller subsystems
'connected' via a tunable coupling is a challenging task relevant for a variety
of physical settings. A general question is whether new phases, not present in
the uncoupled limit, may arise. We use machine learning and a suitable quasidistance
between different points of the phase diagram to study layered spin models, in
which the spin variables constituting each of the uncoupled systems (to which
we refer as layers) are coupled to each other via an interlayer coupling. In such
systems, in general, composite order parameters involving spins of different layers
may emerge as a consequence of the interlayer coupling. We focus on the layered
Ising and Ashkin–Teller models as a paradigmatic case study, determining their
phase diagram via the application of a machine learning algorithm to the Monte
Carlo data. Remarkably our technique is able to correctly characterize all the
system phases also in the case of hidden order parameters, i.e. order parameters
whose expression in terms of the microscopic configurations would require additional
preprocessing of the data fed to the algorithm. We correctly retrieve the three
known phases of the Ashkin–Teller model with ferromagnetic couplings, including
the phase described by a composite order parameter. For the bilayer and trilayer
Ising models the phases we find are only the ferromagnetic and the paramagnetic
ones. Within the approach we introduce, owing to the construction of convolutional
neural networks, naturally suitable for layered image-like data with arbitrary
number of layers, no preprocessing of the Monte Carlo data is needed, also with
regard to its spatial structure. The physical meaning of our results is discussed
and compared with analytical data, where available. Yet, the method can be used
without any a priori knowledge of the phases one seeks to find and can be applied
to other models and structures.
acknowledgement: We thank Gesualdo Delfino, Michele Fabrizio, Piero Ferrarese, Robert
Konik, Christoph Lampert and Mikhail Lemeshko for stimulating discussions at various
stages of this work. WR has received funding from the EU Horizon 2020 program under
the Marie Skłodowska-Curie Grant Agreement No. 665385 and is a recipient of a DOC
Fellowship of the Austrian Academy of Sciences. GB acknowledges support from the
Austrian Science Fund (FWF), under project No. M2641-N27. ND acknowledges support
by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) via Collaborative
Research Center SFB 1225 (ISOQUANT)--project-id 273811115--and under Germany's Excellence
Strategy 'EXC-2181/1-390900948' (the Heidelberg STRUCTURES Excellence Cluster).
article_number: '093026'
article_processing_charge: No
article_type: original
author:
- first_name: Wojciech
full_name: Rzadkowski, Wojciech
id: 48C55298-F248-11E8-B48F-1D18A9856A87
last_name: Rzadkowski
orcid: 0000-0002-1106-4419
- first_name: N
full_name: Defenu, N
last_name: Defenu
- first_name: S
full_name: Chiacchiera, S
last_name: Chiacchiera
- first_name: A
full_name: Trombettoni, A
last_name: Trombettoni
- first_name: Giacomo
full_name: Bighin, Giacomo
id: 4CA96FD4-F248-11E8-B48F-1D18A9856A87
last_name: Bighin
orcid: 0000-0001-8823-9777
citation:
ama: Rzadkowski W, Defenu N, Chiacchiera S, Trombettoni A, Bighin G. Detecting composite
orders in layered models via machine learning. New Journal of Physics.
2020;22(9). doi:10.1088/1367-2630/abae44
apa: Rzadkowski, W., Defenu, N., Chiacchiera, S., Trombettoni, A., & Bighin,
G. (2020). Detecting composite orders in layered models via machine learning.
New Journal of Physics. IOP Publishing. https://doi.org/10.1088/1367-2630/abae44
chicago: Rzadkowski, Wojciech, N Defenu, S Chiacchiera, A Trombettoni, and Giacomo
Bighin. “Detecting Composite Orders in Layered Models via Machine Learning.” New
Journal of Physics. IOP Publishing, 2020. https://doi.org/10.1088/1367-2630/abae44.
ieee: W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, and G. Bighin, “Detecting
composite orders in layered models via machine learning,” New Journal of Physics,
vol. 22, no. 9. IOP Publishing, 2020.
ista: Rzadkowski W, Defenu N, Chiacchiera S, Trombettoni A, Bighin G. 2020. Detecting
composite orders in layered models via machine learning. New Journal of Physics.
22(9), 093026.
mla: Rzadkowski, Wojciech, et al. “Detecting Composite Orders in Layered Models
via Machine Learning.” New Journal of Physics, vol. 22, no. 9, 093026,
IOP Publishing, 2020, doi:10.1088/1367-2630/abae44.
short: W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, G. Bighin, New
Journal of Physics 22 (2020).
date_created: 2020-10-11T22:01:14Z
date_published: 2020-09-01T00:00:00Z
date_updated: 2023-09-07T13:44:16Z
day: '01'
ddc:
- '530'
department:
- _id: MiLe
doi: 10.1088/1367-2630/abae44
ec_funded: 1
external_id:
isi:
- '000573298000001'
file:
- access_level: open_access
checksum: c9238fff422e7a957c3a0d559f756b3a
content_type: application/pdf
creator: dernst
date_created: 2020-10-12T12:18:47Z
date_updated: 2020-10-12T12:18:47Z
file_id: '8650'
file_name: 2020_NewJournalPhysics_Rzdkowski.pdf
file_size: 2725143
relation: main_file
success: 1
file_date_updated: 2020-10-12T12:18:47Z
has_accepted_license: '1'
intvolume: ' 22'
isi: 1
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 05A235A0-7A3F-11EA-A408-12923DDC885E
grant_number: '25681'
name: Analytic and machine learning approaches to composite quantum impurities
- _id: 26986C82-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: M02641
name: A path-integral approach to composite impurities
publication: New Journal of Physics
publication_identifier:
issn:
- '13672630'
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
related_material:
record:
- id: '10759'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Detecting composite orders in layered models via machine learning
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 22
year: '2020'
...
---
_id: '7956'
abstract:
- lang: eng
text: When short-range attractions are combined with long-range repulsions in colloidal
particle systems, complex microphases can emerge. Here, we study a system of isotropic
particles, which can form lamellar structures or a disordered fluid phase when
temperature is varied. We show that, at equilibrium, the lamellar structure crystallizes,
while out of equilibrium, the system forms a variety of structures at different
shear rates and temperatures above melting. The shear-induced ordering is analyzed
by means of principal component analysis and artificial neural networks, which
are applied to data of reduced dimensionality. Our results reveal the possibility
of inducing ordering by shear, potentially providing a feasible route to the fabrication
of ordered lamellar structures from isotropic particles.
article_number: '204905'
article_processing_charge: No
article_type: original
author:
- first_name: J.
full_name: Pȩkalski, J.
last_name: Pȩkalski
- first_name: Wojciech
full_name: Rzadkowski, Wojciech
id: 48C55298-F248-11E8-B48F-1D18A9856A87
last_name: Rzadkowski
orcid: 0000-0002-1106-4419
- first_name: A. Z.
full_name: Panagiotopoulos, A. Z.
last_name: Panagiotopoulos
citation:
ama: 'Pȩkalski J, Rzadkowski W, Panagiotopoulos AZ. Shear-induced ordering in systems
with competing interactions: A machine learning study. The Journal of chemical
physics. 2020;152(20). doi:10.1063/5.0005194'
apa: 'Pȩkalski, J., Rzadkowski, W., & Panagiotopoulos, A. Z. (2020). Shear-induced
ordering in systems with competing interactions: A machine learning study. The
Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0005194'
chicago: 'Pȩkalski, J., Wojciech Rzadkowski, and A. Z. Panagiotopoulos. “Shear-Induced
Ordering in Systems with Competing Interactions: A Machine Learning Study.” The
Journal of Chemical Physics. AIP Publishing, 2020. https://doi.org/10.1063/5.0005194.'
ieee: 'J. Pȩkalski, W. Rzadkowski, and A. Z. Panagiotopoulos, “Shear-induced ordering
in systems with competing interactions: A machine learning study,” The Journal
of chemical physics, vol. 152, no. 20. AIP Publishing, 2020.'
ista: 'Pȩkalski J, Rzadkowski W, Panagiotopoulos AZ. 2020. Shear-induced ordering
in systems with competing interactions: A machine learning study. The Journal
of chemical physics. 152(20), 204905.'
mla: 'Pȩkalski, J., et al. “Shear-Induced Ordering in Systems with Competing Interactions:
A Machine Learning Study.” The Journal of Chemical Physics, vol. 152, no.
20, 204905, AIP Publishing, 2020, doi:10.1063/5.0005194.'
short: J. Pȩkalski, W. Rzadkowski, A.Z. Panagiotopoulos, The Journal of Chemical
Physics 152 (2020).
date_created: 2020-06-14T22:00:49Z
date_published: 2020-05-29T00:00:00Z
date_updated: 2024-02-28T13:00:28Z
day: '29'
department:
- _id: MiLe
doi: 10.1063/5.0005194
ec_funded: 1
external_id:
arxiv:
- '2002.07294'
isi:
- '000537900300001'
intvolume: ' 152'
isi: 1
issue: '20'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1063/5.0005194
month: '05'
oa: 1
oa_version: Published Version
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: The Journal of chemical physics
publication_identifier:
eissn:
- '10897690'
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
record:
- id: '10759'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: 'Shear-induced ordering in systems with competing interactions: A machine learning
study'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 152
year: '2020'
...
---
_id: '415'
abstract:
- lang: eng
text: Recently it was shown that a molecule rotating in a quantum solvent can be
described in terms of the “angulon” quasiparticle [M. Lemeshko, Phys. Rev. Lett.
118, 095301 (2017)]. Here we extend the angulon theory to the case of molecules
possessing an additional spin-1/2 degree of freedom and study the behavior of
the system in the presence of a static magnetic field. We show that exchange of
angular momentum between the molecule and the solvent can be altered by the field,
even though the solvent itself is non-magnetic. In particular, we demonstrate
a possibility to control resonant emission of phonons with a given angular momentum
using a magnetic field.
acknowledgement: "We acknowledge insightful discussions with Giacomo Bighin, Igor
Cherepanov, Johan Mentink, and Enderalp Yakaboylu. This work was supported by the
Austrian Science Fund (FWF), Project No. P29902-N27. W.R. was supported by the Polish
Ministry of Science and Higher Education Grant No. MNISW/2016/DIR/285/NN and by
the European Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska-Curie Grant Agreement No. 665385.\r\n"
article_number: '104307'
article_processing_charge: No
article_type: original
author:
- first_name: Wojciech
full_name: Rzadkowski, Wojciech
id: 48C55298-F248-11E8-B48F-1D18A9856A87
last_name: Rzadkowski
orcid: 0000-0002-1106-4419
- first_name: Mikhail
full_name: Lemeshko, Mikhail
id: 37CB05FA-F248-11E8-B48F-1D18A9856A87
last_name: Lemeshko
orcid: 0000-0002-6990-7802
citation:
ama: Rzadkowski W, Lemeshko M. Effect of a magnetic field on molecule–solvent angular
momentum transfer. The Journal of Chemical Physics. 2018;148(10). doi:10.1063/1.5017591
apa: Rzadkowski, W., & Lemeshko, M. (2018). Effect of a magnetic field on molecule–solvent
angular momentum transfer. The Journal of Chemical Physics. AIP Publishing.
https://doi.org/10.1063/1.5017591
chicago: Rzadkowski, Wojciech, and Mikhail Lemeshko. “Effect of a Magnetic Field
on Molecule–Solvent Angular Momentum Transfer.” The Journal of Chemical Physics.
AIP Publishing, 2018. https://doi.org/10.1063/1.5017591.
ieee: W. Rzadkowski and M. Lemeshko, “Effect of a magnetic field on molecule–solvent
angular momentum transfer,” The Journal of Chemical Physics, vol. 148,
no. 10. AIP Publishing, 2018.
ista: Rzadkowski W, Lemeshko M. 2018. Effect of a magnetic field on molecule–solvent
angular momentum transfer. The Journal of Chemical Physics. 148(10), 104307.
mla: Rzadkowski, Wojciech, and Mikhail Lemeshko. “Effect of a Magnetic Field on
Molecule–Solvent Angular Momentum Transfer.” The Journal of Chemical Physics,
vol. 148, no. 10, 104307, AIP Publishing, 2018, doi:10.1063/1.5017591.
short: W. Rzadkowski, M. Lemeshko, The Journal of Chemical Physics 148 (2018).
date_created: 2018-12-11T11:46:21Z
date_published: 2018-03-14T00:00:00Z
date_updated: 2024-02-28T13:01:59Z
day: '14'
department:
- _id: MiLe
doi: 10.1063/1.5017591
ec_funded: 1
external_id:
arxiv:
- '1711.09904'
isi:
- '000427517200065'
intvolume: ' 148'
isi: 1
issue: '10'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1711.09904
month: '03'
oa: 1
oa_version: Preprint
project:
- _id: 26031614-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P29902
name: Quantum rotations in the presence of a many-body environment
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: The Journal of Chemical Physics
publication_status: published
publisher: AIP Publishing
publist_id: '7408'
quality_controlled: '1'
related_material:
record:
- id: '10759'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Effect of a magnetic field on molecule–solvent angular momentum transfer
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 148
year: '2018'
...