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Computational and Algorithmic Approaches for Studying Exotic Spin Textures
KTH, School of Engineering Sciences (SCI), Applied Physics. KTH, Centres, SeRC - Swedish e-Science Research Centre. (Anna Delin)ORCID iD: 0000-0002-1229-4902
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Exotic spin textures such as skyrmions, are proved to be promising candidates for the development of sustainable, next-generation spintronic devices. Despite extensive research in this domain, the quest for efficient computational methodologies for the automated discovery of novel functional magnetic materials, and identify the intricate topological spin textures they can host, remains a formidable challenge in solid-state physics. This thesis work introduces a promising end-to-end computational approach, employing the Heisenberg spin Hamiltonian model, aimed at overcoming this challenge and discovering novel exotic spin textures. Our approaches encompass an automated Density Functional Theory (DFT) calculation workflow designed to predict candidate functional magnetic materials for hosting spin textures, with those candidate material we calculate their magnetic exchange interactions for constructing the spin Hamiltonian. Subsequently, a computational workflow that integrates new developed metaheuristic optimization algorithms with Atomistic Spin Dynamics (ASD) simulations is employed to identify spin textures in targeted systems. Additionally, a post-processing tool for the visualization of these textures is presented.

In the computational approach part of this work, we have developed several tools, including the high-throughput workflow code and the scientific visualization software, dedicated to studying exotic spin textures. On the algorithmic front, we introduce the metaheuristic conditional neural network and a controlled assembly approach for investigating the long-lifetime metastable states of magnetic systems with long-range interactions. Through these novel approaches, we have identified and predicted the constructing pathways to several novel high-order antiskyrmions and three types of skyrmionic metamaterials (i.e., lattice-like, flake-like, and cell-like). Furthermore, we applied evolutionary algorithms for identifying the ground states of skyrmionic systems, developing both the genetic tunneling optimizer (GTO) and a neuroevolutionary algorithm.

In summary, the main results are:

1. Discovery and analysis of the forming mechanism of novel high-order antiskyrmions in the PdFeIr system.

2. Introduction of the evolutionary algorithm to the atomistic spin Hamiltonian model for the first time, combined with Markov chain Monte Carlo and atomistic spin dynamics simulations.

3. Prediction of skyrmions in 4d and 5d doped B20 systems.

4. Discovery and revealing the construction pathway of new skyrmionic textures, i.e., 2D skyrmionic metamaterials.

5. Development of a general visualization and post-processing code for computational magnetism, which offers new opportunities for analyzing complex spin textures.

Abstract [sv]

Exotiska spinntexturer såsom skyrmioner, är lovande kandidater för utvecklingen av nästa generations, hållbara spinntronik-enheter. Trots omfattande forskning inom detta område är sökandet efter effektiva beräkningsmetoder för den automatiserade upptäckten av nya funktionella magnetiska material och identifiering av de komplexa topologiska spintexturer de kan hysa, fortfarande en formidabel utmaning inom fasta tillståndets fysik. Detta avhandlingsarbete introducerar en lovande helhetslösning för beräkning, som använder Heisenberg spinn Hamiltonian-modellen, inriktad på att öve-rvinna denna utmaning och upptäcka nya exotiska spintexturer. Våra meto- der omfattar ett automatiserat arbetsflöde för beräkningar med täthetsfunkt- ionalteori (DFT)  som är utformat för att förutsäga kandidatmaterial för funktionella magnetiska material som kan hysa spinntexturer, med dessa kandidatmaterial beräknar vi deras magnetiska utbytesväxelverkan för att konstruera spinn-Hamiltonoperatorer. Därefter används ett beräkningsarbet- sflöde som integrerar nyligen utvecklade metaheuristiska optimeringsalgoritmer med atomistisk spinndynamik (ASD) simuleringar för att identifiera spinntexturer i riktade system. Vidare presenteras ett efterbehandlingsverktyg för visualisering av dessa texturer.

I den beräkningsmässiga delen av detta arbete har vi utvecklat flera verktyg, inklusive programvara för arbetsflöden med hög genomströmning, samt för vetenskaplig visualisering, tillägnad studier exotiska spintexturer. På den algoritmiska sidan introducerar vi det metaheuristiska villkorade neuronnätverket och en metod för kontrollerad sammansättning för att undersöka de långlivade metastabila tillstånden hos magnetiska system med växelverkan av lång räckvidd. Genom dessa nya metoder har vi identifierat konstruktionsvägar till flera nya högre ordningens antiskyrmioner och tre typer av skyrmioniska metamaterial (dvs. gitterliknande, flagliknande och celliknande). Dessutom var vi i tillämpningen av evolutionära algoritmer för att identifiera grundtillstånden hos skyrmioniska system, och utvecklade både den genetiska tunneloptimeraren (GTO) och en neuroevolutionär algoritm.

Sammanfattning av de huvudsakliga resultaten:

1. Upptäckt och analys av bildningsmekanismen för nya högordnings antiskyrmioner i PdFeIr-systemet.

2. Förutsägelse av skyrmioner i 4d- och 5d-dopade B20-system.

3. Första introduktionen av evolutionära algoritmer till atomistiska spinn-Hamiltonoperator, kombinerat med Markovkedje Monte Carlo och atomistiska spinndynamiksimuleringar.

4. Upptäckt av konstruktionsvägen för nya skyrmioniska texturer, exempelvis 2D skyrmioniska metamaterial.

5. Utveckling av en allmän visualiserings- och efterbehandlingskod för beräkningsmagnetism, vilket erbjuder nya möjligheter för analys av komplexa spinntexturer.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2024.
Series
TRITA-SCI-FOU ; 2024:26
National Category
Natural Sciences Physical Sciences Condensed Matter Physics
Research subject
Physics, Material and Nano Physics; SRA - E-Science (SeRC); Physics, Theoretical Physics
Identifiers
URN: urn:nbn:se:kth:diva-346223ISBN: 978-91-8040-946-9 (print)OAI: oai:DiVA.org:kth-346223DiVA, id: diva2:1856534
Public defence
2024-05-24, 4204, Albanovägen 29, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 2024-05-07

Available from: 2024-05-07 Created: 2024-05-07 Last updated: 2024-05-20Bibliographically approved
List of papers
1. Metaheuristic conditional neural network for harvesting skyrmionic metastable states
Open this publication in new window or tab >>Metaheuristic conditional neural network for harvesting skyrmionic metastable states
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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 (APS), 2023
National Category
Condensed Matter Physics
Identifiers
urn:nbn:se:kth:diva-344103 (URN)10.1103/PhysRevResearch.5.043199 (DOI)001128824200002 ()2-s2.0-85179004348 (Scopus ID)
Note

QC 20240301

Available from: 2024-03-01 Created: 2024-03-01 Last updated: 2024-05-07Bibliographically approved
2. Genetic-tunneling driven energy optimizer for spin systems
Open this publication in new window or tab >>Genetic-tunneling driven energy optimizer for spin systems
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2023 (English)In: Communications Physics, E-ISSN 2399-3650, Vol. 6, no 1, article id 239Article in journal (Refereed) Published
Abstract [en]

Finding the ground state of complex many-body systems, such as magnetic materials containing topological textures, like skyrmions, is a fundamental and long-standing problem. We present here a genetic-tunneling-driven variance-controlled optimization method, that efficiently identifies the ground state of two-dimensional skyrmionic systems. The approach combines a local energy-minimizer backend and a metaheuristic global search frontend. The method is shown to perform significantly better than simulated annealing. Specifically, we demonstrate that for the Pd/Fe/Ir(111) system, our method correctly and efficiently identifies the experimentally observed spin spiral geometry, skyrmion lattice and ferromagnetic ground states as a function of the external magnetic field. To our knowledge, no other optimization method has until now succeeded in doing this. We envision that our findings will pave the way for evolutionary computing in mapping out phase diagrams for spin systems in general.

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Condensed Matter Physics
Identifiers
urn:nbn:se:kth:diva-336301 (URN)10.1038/s42005-023-01360-4 (DOI)2-s2.0-85169699856 (Scopus ID)
Note

QC 20230913

Available from: 2023-09-13 Created: 2023-09-13 Last updated: 2024-05-07Bibliographically approved
3. Tuning skyrmions in B20 compounds by 4d and 5d doping
Open this publication in new window or tab >>Tuning skyrmions in B20 compounds by 4d and 5d doping
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2022 (English)In: Physical Review Materials, E-ISSN 2475-9953, Vol. 6, no 8, article id 084401Article in journal (Refereed) Published
Abstract [en]

Skyrmion stabilization in novel magnetic systems with the B20 crystal structure is reported here, primarily based on theoretical results. The focus is on the effect of alloying on the 3d sublattice of the B20 structure by substitution of heavier 4d and 5d elements, with the ambition to tune the spin-orbit coupling and its influence on magnetic interactions. State-of-the-art methods based on density functional theory are used to calculate both isotropic and anisotropic exchange interactions. Significant enhancement of the Dzyaloshinskii-Moriya interaction is reported for 5d-doped FeSi and CoSi, accompanied by a large modification of the spin stiffness and spiralization. Micromagnetic simulations coupled to atomistic spin-dynamics and ab initio magnetic interactions reveal the spin-spiral nature of the magnetic ground state and field-induced skyrmions for all these systems. Especially small skyrmions similar to 50 nm are predicted for Co0.75Os0.25Si, compared to similar to 148 nm for Fe0.75Co0.25Si. Convex-hull analysis suggests that all B20 compounds considered here are structurally stable at elevated temperatures and should be possible to synthesize. This prediction is confirmed experimentally by synthesis and structural analysis of the Ru-doped CoSi systems discussed here, both in powder and in single-crystal forms.

Place, publisher, year, edition, pages
American Physical Society (APS), 2022
National Category
Nano Technology Condensed Matter Physics Atom and Molecular Physics and Optics
Identifiers
urn:nbn:se:kth:diva-316724 (URN)10.1103/PhysRevMaterials.6.084401 (DOI)000838143600001 ()2-s2.0-85137268142 (Scopus ID)
Note

QC 20220830

Available from: 2022-08-30 Created: 2022-08-30 Last updated: 2024-05-07Bibliographically approved
4. Design of 2D Skyrmionic Metamaterial Through Controlled Assembly
Open this publication in new window or tab >>Design of 2D Skyrmionic Metamaterial Through Controlled Assembly
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Despite extensive research on magnetic skyrmions and antiskyrmions, a significant challenge remains in crafting nontrivial high-order skyrmionic textures with varying, or even tailor-made, topologies. We address this challenge, by focusing on a construction pathway of skyrmionics metamaterial within a monolayer thin film and suggest several promising lattice-like, flakes-like, and cell-like skyrmionic metamaterials that are surprisingly stable. Central to our approach is the concept of 'simulated controlled assembly', in short, a protocol inspired by 'click chemistry' that allows for positioning topological magnetic structures where one likes, and then allowing for energy minimization to elucidate the stability. Utilizing high-throughput atomistic-spin-dynamic (ASD) simulations alongside state-of-the-art AI-driven tools, we have isolated skyrmions (topological charge Q=1), antiskyrmions (Q=-1), and skyrmionium (Q=0). These entities serve as foundational 'skyrmionic building blocks' to forming reported intricate textures. In this work, two key contributions are introduced to the field of skyrmionic systems. First, we present a novel method for integrating control assembly protocols for the stabilization and investigation of topological magnets, which marks a significant advancement in the ability to explore new skyrmionic textures. Second, we report on the discovery of skyrmionic metamaterials, which shows a plethora of complex topologies that are possible to investigate theoretically and experimentally.

National Category
Natural Sciences
Research subject
Physics, Material and Nano Physics; Physics, Theoretical Physics
Identifiers
urn:nbn:se:kth:diva-346220 (URN)
Note

QC 20240507

Available from: 2024-05-07 Created: 2024-05-07 Last updated: 2024-05-07Bibliographically approved
5. SpinView: General Interactive Visual Analysis Tool for Multiscale Computational Magnetism
Open this publication in new window or tab >>SpinView: General Interactive Visual Analysis Tool for Multiscale Computational Magnetism
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Multiscale magnetic simulations, including micromagnetic and atomistic spin dynamics simulations, are widely used in the study of complex magnetic systems over a wide range of spatial and temporal scales. The advances in these simulation technologies have generated considerable amounts of data. However, a versatile and general tool for visualization, filtering, and denoising this data is largely lacking. To overcome these limitations, we have developed SpinView, a general interactive visual analysis tool for graphical exploration and data distillation. Combined with dynamic filters and a built-in database, it is possible to generate reproducible publication-quality images, videos, or portable interactive webpages within seconds. Since the basic input to SpinView is a vector field, it can be directly integrated with any spin dynamics simulation tool. With minimal effort on the part of the user, SpinView delivers a simplified workflow, speeds up analysis of complex datasets and trajectories, and enables new types of analysis and insight.

National Category
Physical Sciences
Research subject
Physics, Material and Nano Physics; Physics, Theoretical Physics
Identifiers
urn:nbn:se:kth:diva-346219 (URN)
Note

QC 20240507

Available from: 2024-05-07 Created: 2024-05-07 Last updated: 2024-05-07Bibliographically approved
6. Neuroevolutionary optimization for magnetic ground state
Open this publication in new window or tab >>Neuroevolutionary optimization for magnetic ground state
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Stable magnetic states, including particle-like topological protected magnetic textures like skyrmions and hopfions, have recently shown a great contribution in various fields of science. In order to identify these textures in unknown magnetic systems, we have developed a hybrid neuroevolutionary algorithm based on shallow neural network to find stable magnetic states in Heisenberg spin model. Our algorithm combines global and local searching ability of both genetic algorithm (GA) and gradient descent(GD), and has no need of prior knowledge. It has capability of escaping from the local minimum in the potential energy surface (PES) by treating the topologically protected states as chromosomes and making genetic tunneling. It then navigates the spin configuration of the ground state of the magnetic Hamiltonian. We achieve great improvements in robustness and effectiveness compared to the traditional approaches like heat bath simulated annealing at zero Kelvin. These features make this algorithm as a promising tool for solving global optimization problems in magnetic systems.

National Category
Natural Sciences
Identifiers
urn:nbn:se:kth:diva-346221 (URN)
Note

QC 20240508

Available from: 2024-05-07 Created: 2024-05-07 Last updated: 2024-05-08Bibliographically approved

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