---
_id: '9679'
abstract:
- lang: eng
text: The relative motion of three impenetrable particles on a ring, in our case
two identical fermions and one impurity, is isomorphic to a triangular quantum
billiard. Depending on the ratio κ of the impurity and fermion masses, the billiards
can be integrable or non-integrable (also referred to in the main text as chaotic).
To set the stage, we first investigate the energy level distributions of the billiards
as a function of 1/κ ∈ [0, 1] and find no evidence of integrable cases beyond
the limiting values 1/κ = 1 and 1/κ = 0. Then, we use machine learning tools to
analyze properties of probability distributions of individual quantum states.
We find that convolutional neural networks can correctly classify integrable and
non-integrable states. The decisive features of the wave functions are the normalization
and a large number of zero elements, corresponding to the existence of a nodal
line. The network achieves typical accuracies of 97%, suggesting that machine
learning tools can be used to analyze and classify the morphology of probability
densities obtained in theory or experiment.
acknowledgement: We thank Aidan Tracy for his input during the initial stages of this
project. We thank Nathan Harshman, Achim Richter, Wojciech Rzadkowski, and Dane
Hudson Smith for helpful discussions and comments on the manuscript. This work has
been supported by European Union's Horizon 2020 research and innovation program
under the Marie Skłodowska-Curie Grant Agreement No. 754411 (AGV); by the German
Aeronautics and Space Administration (DLR) through Grant No. 50 WM 1957 (OVM); by
the Deutsche Forschungsgemeinschaft through Project VO 2437/1-1 (Project No. 413495248)
(AGV and HWH); by the Deutsche Forschungsgemeinschaft through Collaborative Research
Center SFB 1245 (Project No. 279384907) and by the Bundesministerium für Bildung
und Forschung under Contract 05P18RDFN1 (HWH). HWH also thanks the ECT* for hospitality
during the workshop 'Universal physics in Many-Body Quantum Systems—From Atoms to
Quarks'. This infrastructure is part of a project that has received funding from
the European Union's Horizon 2020 research and innovation program under Grant Agreement
No. 824093. We acknowledge support by the Deutsche Forschungsgemeinschaft and the
Open Access Publishing Fund of Technische Universität Darmstadt.
article_number: '065009'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: David
full_name: Huber, David
last_name: Huber
- first_name: Oleksandr V.
full_name: Marchukov, Oleksandr V.
last_name: Marchukov
- first_name: Hans Werner
full_name: Hammer, Hans Werner
last_name: Hammer
- first_name: Artem
full_name: Volosniev, Artem
id: 37D278BC-F248-11E8-B48F-1D18A9856A87
last_name: Volosniev
orcid: 0000-0003-0393-5525
citation:
ama: Huber D, Marchukov OV, Hammer HW, Volosniev A. Morphology of three-body quantum
states from machine learning. New Journal of Physics. 2021;23(6). doi:10.1088/1367-2630/ac0576
apa: Huber, D., Marchukov, O. V., Hammer, H. W., & Volosniev, A. (2021). Morphology
of three-body quantum states from machine learning. New Journal of Physics.
IOP Publishing. https://doi.org/10.1088/1367-2630/ac0576
chicago: Huber, David, Oleksandr V. Marchukov, Hans Werner Hammer, and Artem Volosniev.
“Morphology of Three-Body Quantum States from Machine Learning.” New Journal
of Physics. IOP Publishing, 2021. https://doi.org/10.1088/1367-2630/ac0576.
ieee: D. Huber, O. V. Marchukov, H. W. Hammer, and A. Volosniev, “Morphology of
three-body quantum states from machine learning,” New Journal of Physics,
vol. 23, no. 6. IOP Publishing, 2021.
ista: Huber D, Marchukov OV, Hammer HW, Volosniev A. 2021. Morphology of three-body
quantum states from machine learning. New Journal of Physics. 23(6), 065009.
mla: Huber, David, et al. “Morphology of Three-Body Quantum States from Machine
Learning.” New Journal of Physics, vol. 23, no. 6, 065009, IOP Publishing,
2021, doi:10.1088/1367-2630/ac0576.
short: D. Huber, O.V. Marchukov, H.W. Hammer, A. Volosniev, New Journal of Physics
23 (2021).
date_created: 2021-07-18T22:01:22Z
date_published: 2021-06-23T00:00:00Z
date_updated: 2023-08-10T13:58:09Z
day: '23'
ddc:
- '530'
department:
- _id: MiLe
doi: 10.1088/1367-2630/ac0576
ec_funded: 1
external_id:
arxiv:
- '2102.04961'
isi:
- '000664736300001'
file:
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checksum: e39164ce7ea228d287cf8924e1a0f9fe
content_type: application/pdf
creator: cziletti
date_created: 2021-07-19T11:47:16Z
date_updated: 2021-07-19T11:47:16Z
file_id: '9690'
file_name: 2021_NewJPhys_Huber.pdf
file_size: 3868445
relation: main_file
success: 1
file_date_updated: 2021-07-19T11:47:16Z
has_accepted_license: '1'
intvolume: ' 23'
isi: 1
issue: '6'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: New Journal of Physics
publication_identifier:
eissn:
- '13672630'
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Morphology of three-body quantum states from 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)
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type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 23
year: '2021'
...
---
_id: '10178'
abstract:
- lang: eng
text: In dense biological tissues, cell types performing different roles remain
segregated by maintaining sharp interfaces. To better understand the mechanisms
for such sharp compartmentalization, we study the effect of an imposed heterotypic
tension at the interface between two distinct cell types in a fully 3D Voronoi
model for confluent tissues. We find that cells rapidly sort and self-organize
to generate a tissue-scale interface between cell types, and cells adjacent to
this interface exhibit signature geometric features including nematic-like ordering,
bimodal facet areas, and registration, or alignment, of cell centers on either
side of the two-tissue interface. The magnitude of these features scales directly
with the magnitude of the imposed tension, suggesting that biologists can estimate
the magnitude of tissue surface tension between two tissue types simply by segmenting
a 3D tissue. To uncover the underlying physical mechanisms driving these geometric
features, we develop two minimal, ordered models using two different underlying
lattices that identify an energetic competition between bulk cell shapes and tissue
interface area. When the interface area dominates, changes to neighbor topology
are costly and occur less frequently, which generates the observed geometric features.
acknowledgement: "We thank Paula Sanematsu, Matthias Merkel, Daniel Sussman, Cristina
Marchetti and Edouard Hannezo for helpful discussions, and M Merkel for developing
and sharing the original version of the 3D Voronoi code. This work was primarily
funded by NSF-PHY-1607416, NSF-PHY-2014192 , and are in the division of physics
at the National Science Foundation. PS and MLM acknowledge additional support from
Simons Grant No. 454947.\r\n"
article_number: '093043'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Preeti
full_name: Sahu, Preeti
id: 55BA52EE-A185-11EA-88FD-18AD3DDC885E
last_name: Sahu
- first_name: J. M.
full_name: Schwarz, J. M.
last_name: Schwarz
- first_name: M. Lisa
full_name: Manning, M. Lisa
last_name: Manning
citation:
ama: Sahu P, Schwarz JM, Manning ML. Geometric signatures of tissue surface tension
in a three-dimensional model of confluent tissue. New Journal of Physics.
2021;23(9). doi:10.1088/1367-2630/ac23f1
apa: Sahu, P., Schwarz, J. M., & Manning, M. L. (2021). Geometric signatures
of tissue surface tension in a three-dimensional model of confluent tissue. New
Journal of Physics. IOP Publishing. https://doi.org/10.1088/1367-2630/ac23f1
chicago: Sahu, Preeti, J. M. Schwarz, and M. Lisa Manning. “Geometric Signatures
of Tissue Surface Tension in a Three-Dimensional Model of Confluent Tissue.” New
Journal of Physics. IOP Publishing, 2021. https://doi.org/10.1088/1367-2630/ac23f1.
ieee: P. Sahu, J. M. Schwarz, and M. L. Manning, “Geometric signatures of tissue
surface tension in a three-dimensional model of confluent tissue,” New Journal
of Physics, vol. 23, no. 9. IOP Publishing, 2021.
ista: Sahu P, Schwarz JM, Manning ML. 2021. Geometric signatures of tissue surface
tension in a three-dimensional model of confluent tissue. New Journal of Physics.
23(9), 093043.
mla: Sahu, Preeti, et al. “Geometric Signatures of Tissue Surface Tension in a Three-Dimensional
Model of Confluent Tissue.” New Journal of Physics, vol. 23, no. 9, 093043,
IOP Publishing, 2021, doi:10.1088/1367-2630/ac23f1.
short: P. Sahu, J.M. Schwarz, M.L. Manning, New Journal of Physics 23 (2021).
date_created: 2021-10-24T22:01:34Z
date_published: 2021-09-29T00:00:00Z
date_updated: 2023-08-14T08:10:31Z
day: '29'
ddc:
- '570'
department:
- _id: EdHa
doi: 10.1088/1367-2630/ac23f1
external_id:
arxiv:
- '2102.05397'
isi:
- '000702042400001'
file:
- access_level: open_access
checksum: ace603e8f0962b3ba55f23fa34f57764
content_type: application/pdf
creator: cziletti
date_created: 2021-10-28T12:06:01Z
date_updated: 2021-10-28T12:06:01Z
file_id: '10193'
file_name: 2021_NewJPhys_Sahu.pdf
file_size: 2215016
relation: main_file
success: 1
file_date_updated: 2021-10-28T12:06:01Z
has_accepted_license: '1'
intvolume: ' 23'
isi: 1
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
publication: New Journal of Physics
publication_identifier:
eissn:
- '13672630'
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Geometric signatures of tissue surface tension in a three-dimensional model
of confluent tissue
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: 23
year: '2021'
...