Metaheuristic conditional neural network for harvesting skyrmionic metastable statesShow others and affiliations
2023 (English)In: Physical Review Research, E-ISSN 2643-1564, Vol. 5, no 4, article id 043199Article in journal (Refereed) Published
Abstract [en]
We present a metaheuristic conditional neural-network-based method aimed at identifying physically interest-ing metastable states in a potential energy surface of high rugosity. To demonstrate how this method works, we identify and analyze spin textures with topological charge Q ranging from 1 to -13 (where antiskyrmions have Q < 0) in the Pd/Fe/Ir(111) system, which we model using a classical atomistic spin Hamiltonian based on parameters computed from density functional theory. To facilitate the harvest of relevant spin textures, we make use of the newly developed segment anything model. Spin textures with Q ranging from -3 to -6 are further analyzed using finite-temperature spin-dynamics simulations. We observe that for temperatures up to around 20 K, lifetimes longer than 200 ps are predicted, and that when these textures decay, new topological spin textures are formed. We also find that the relative stability of the spin textures depend linearly on the topological charge, but only when comparing the most stable antiskyrmions for each topological charge. In general, the number of holes (i.e., non-self-intersecting curves that define closed domain walls in the structure) in the spin texture is an important predictor of stability-the more holes, the less stable the texture. Methods for systematic identification and characterization of complex metastable skyrmionic textures-such as the one demonstrated here-are highly relevant for advancements in the field of topological spintronics.
Place, publisher, year, edition, pages
American Physical Society , 2023. Vol. 5, no 4, article id 043199
National Category
Condensed Matter Physics
Identifiers
URN: urn:nbn:se:oru:diva-112070DOI: 10.1103/PhysRevResearch.5.043199ISI: 001128824200002Scopus ID: 2-s2.0-85179004348OAI: oai:DiVA.org:oru-112070DiVA, id: diva2:1842019
Funder
Knut and Alice Wallenberg Foundation, 2018.0060; 2021.0246; 2022.0108Swedish Research Council, 2017-03832; 2019-03666; 2016-05980; 2019- 05304; 2020-05110; 2018-05973StandUpeSSENCE - An eScience CollaborationSwedish National Infrastructure for Computing (SNIC)
Note
This work was financially supported by the Knut and Alice Wallenberg Foundation (Grants No. 2018.0060, No. 2021.0246, and No. 2022.0108), Vetenskapsradet (Grants No. 2017-03832, No. 2019-03666, No. 2016-05980, No. 2019- 05304, and No. 2020-05110), the Icelandic Research Fund (Grant No. 217750), the University of Iceland Research Fund (Grant No. 15673), the European Research Council (Grant No. 854843-FASTCORR), the foundation for Strategic Research SSF, and China Science Council (CSC) Grant No. 201906920083. Support from STandUP, Digital Futures, SeRC, ER (project FASTCORR-Grant No. 854843), and eSSENCE is also acknowledged. Computations/data handling were enabled by resources provided by KAW (Berzelius-2022-259) and the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through Grant Agreement No. 2018-05973.
2024-03-012024-03-012024-03-01Bibliographically approved