Open Access
Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas
Publication type: Journal Article
Publication date: 2023-05-16
scimago Q1
wos Q1
SJR: 2.856
CiteScore: 15.6
Impact factor: 9.0
ISSN: 00319007, 10797114
PubMed ID:
37267577
General Physics and Astronomy
Abstract
An artificial neural network is used to determine the phase diagram of strongly correlated fermions across the BCS-BEC crossover, revealing a maximum in the critical temperature at the bosonic side.
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Metrics
17
Total citations:
17
Citations from 2024:
12
(70.58%)
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GOST
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Link M. et al. Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas // Physical Review Letters. 2023. Vol. 130. No. 20. 203401
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Link M., Gao K., Kell A., Breyer M., Eberz D., Rauf B., Köhl M. Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas // Physical Review Letters. 2023. Vol. 130. No. 20. 203401
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RIS
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TY - JOUR
DO - 10.1103/physrevlett.130.203401
UR - https://doi.org/10.1103/physrevlett.130.203401
TI - Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas
T2 - Physical Review Letters
AU - Link, M
AU - Gao, Kuiyi
AU - Kell, Andreas
AU - Breyer, M
AU - Eberz, D.
AU - Rauf, B
AU - Köhl, Michael
PY - 2023
DA - 2023/05/16
PB - American Physical Society (APS)
IS - 20
VL - 130
PMID - 37267577
SN - 0031-9007
SN - 1079-7114
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2023_Link,
author = {M Link and Kuiyi Gao and Andreas Kell and M Breyer and D. Eberz and B Rauf and Michael Köhl},
title = {Machine Learning the Phase Diagram of a Strongly Interacting Fermi Gas},
journal = {Physical Review Letters},
year = {2023},
volume = {130},
publisher = {American Physical Society (APS)},
month = {may},
url = {https://doi.org/10.1103/physrevlett.130.203401},
number = {20},
pages = {203401},
doi = {10.1103/physrevlett.130.203401}
}