Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Self-organizing maps as a method for detecting phase transitions and phase identification
TTMO Univ, St Petersburg 197101, Russia..
TTMO Univ, St Petersburg 197101, Russia.;Univ Iceland, Sci Inst, Dunhagi 3, IS-107 Reykjavik, Iceland..
KTH, School of Engineering Sciences (SCI), Physics.ORCID iD: 0000-0003-0210-4340
Uppsala Univ, Dept Phys & Astron, Mat Theory Div, Box 516, SE-75120 Uppsala, Sweden..
Show others and affiliations
2019 (English)In: Physical Review B, ISSN 2469-9950, E-ISSN 2469-9969, Vol. 99, no 4, article id 041108Article in journal (Refereed) Published
Abstract [en]

Originating from image recognition, methods of machine learning allow for effective feature extraction and dimensionality reduction in multidimensional datasets, thereby providing an extraordinary tool to deal with classical and quantum models in many-body physics. In this study, we employ a specific unsupervised machine learning technique-self-organizing maps-to create a low-dimensional representation of microscopic states, relevant for macroscopic phase identification and detecting phase transitions. We explore the properties of spin Hamiltonians of two archetype model systems: a two-dimensional Heisenberg ferromagnet and a three-dimensional crystal, Fe in the body-centered-cubic structure. The method of self-organizing maps, which is known to conserve connectivity of the initial dataset, is compared to the cumulant method theory and is shown to be as accurate while being computationally more efficient in determining a phase transition temperature. We argue that the method proposed here can be applied to explore a broad class of second-order phase-transition systems, not only magnetic systems but also, for example, order-disorder transitions in alloys.

Place, publisher, year, edition, pages
American Physical Society, 2019. Vol. 99, no 4, article id 041108
National Category
Other Physics Topics
Identifiers
URN: urn:nbn:se:kth:diva-242980DOI: 10.1103/PhysRevB.99.041108ISI: 000455825600002Scopus ID: 2-s2.0-85059904004OAI: oai:DiVA.org:kth-242980DiVA, id: diva2:1285408
Funder
EU, Horizon 2020Swedish Research CouncilKnut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research Swedish Energy AgencyeSSENCE - An eScience CollaborationStandUp
Note

QC 20190204

Available from: 2019-02-04 Created: 2019-02-04 Last updated: 2019-02-04Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Hellsvik, Johan

Search in DiVA

By author/editor
Hellsvik, Johan
By organisation
Physics
In the same journal
Physical Review B
Other Physics Topics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 5 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf