Persistent homology as efficient phase descriptor for 2D skyrmion lattices

dc.contributor.authorTaniwaki, Michiki
dc.contributor.authorWinkler, Thomas Brian
dc.contributor.authorRothörl, Jan
dc.contributor.authorGruber, Raphael
dc.contributor.authorMitsumata, Chiharu
dc.contributor.authorKotsugi, Masato
dc.contributor.authorKläui, Mathias
dc.date.accessioned2026-08-11T07:24:42Z
dc.date.issued2026
dc.description.abstractTwo-dimensional (2D) particle systems, such as magnetic skyrmions, exhibit topological phase transitions between unique 2D phases. However, a simple and computationally efficient methodology to capture lattice configurational properties and construct an appropriate, easily calculable indicator for phase identification remains elusive. Here, we propose an indicator for topological phase transitions using persistent homology (PH). PH provides a complementary topological description by capturing persistent features derived from the configurational properties of the lattice. The proposed persistent-homology-based indicator, which selectively counts stable features in a persistence diagram, effectively traces the lattice’s ordering changes, as confirmed by comparisons with the conventionally used measure of the ordering (the magnitude of the orientational order parameter ⟨|Ψ6|⟩), typically used to identify lattice phases. We demonstrate the applicability of our indicator to experimental data, showing that it yields results consistent with those of simulations. This experimental validation highlights the robustness of the proposed method for real physical systems beyond idealized simulated systems. While our method is demonstrated in the context of skyrmion lattice systems, the approach is general and can be extended to other two-dimensional systems composed of interacting particles. The proposed topological indicator remains computationally tractable for the system sizes considered here, while preserving the topological invariant-based structural information.en_GB
dc.description.sponsorship(Deutsche Forschungsgemeinschaft|403502522, Deutsche Forschungsgemeinschaft|49741853, Deutsche Forschungsgemeinschaft|268565370, National Research Council of Science and Technology|GTL24041-000, Horizon 2020 Framework Program|863155, Horizon 2020 Framework Program|856538, HORIZON EUROPE Framework Program|101070290, Ministry of Science and ICT|GTL24041-000)
dc.identifier.doihttps://doi.org/10.25358/openscience-16074
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/16095
dc.language.isoeng
dc.rightsCC-BY-4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc530 Physikde_DE
dc.subject.ddc530 Physicsen_GB
dc.titlePersistent homology as efficient phase descriptor for 2D skyrmion latticesen_GB
dc.typeZeitschriftenaufsatzde_DE
elements.depositor.primary-group-descriptorFachbereich Physik, Mathematik und Informatik
elements.object.id298933
elements.object.typejournal-article
jgu.identifier.uuid02f0472c-c6f9-4697-94f0-c4f407a7c44b
jgu.journal.issue2
jgu.journal.titleAPL machine learning
jgu.journal.volume4
jgu.organisation.departmentFB 08 Physik, Mathematik u. Informatikde_DE
jgu.organisation.nameJohannes Gutenberg-Universität Mainzde_DE
jgu.organisation.number7940
jgu.organisation.placeMainz
jgu.organisation.rorhttps://ror.org/023b0x485
jgu.pages.alternative026111
jgu.publisher.doi10.1063/5.0331214
jgu.publisher.eissn2770-9019
jgu.publisher.nameAIP Publishing
jgu.publisher.placeMelville, NY
jgu.publisher.year2026
jgu.relation.IsVersionOf10.1063/5.0331214
jgu.rights.accessrightsopenAccessen_GB
jgu.subject.ddccode530
jgu.type.dinitypeArticleen_GB
jgu.type.resourceTexten_GB
jgu.type.versionPublished versionen_GB

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