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For many materials, a precise knowledge of their dispersion spectra is insufficient to predict their ordered phases and physical responses. Instead, these materials are classified by the geometrical and topological properties of their…

Materials Science · Physics 2021-06-16 Qiong Ma , Adolfo G. Grushin , Kenneth S. Burch

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new…

Topological magnetic textures - like skyrmions - have become a major player in the design of next-generation magnetic storage technology due to their stability and the control of their motion by ultra-low current densities. A major…

Strongly Correlated Electrons · Physics 2017-09-26 Karin Everschor-Sitte , Matthias Sitte , Thierry Valet , Ar. Abanov , Jairo Sinova

Skyrmions are topologically stable vector textures as potential information carriers for high-density data storage and communications, especially boosted by the recently emerging meta-generators of skyrmions in electromagnetic fields.…

We investigate the interplay of geometric symmetry, size, and external magnetic fields in regulating individual skyrmion states within magnetic nanostructures. By analyzing nanodisks, nanosquares, and nanorectangles, we demonstrate that…

Mesoscale and Nanoscale Physics · Physics 2026-05-19 J. Y. Wang , C. X. Zhao , Y. F. Duan , H. M. Dong

We report the creation and real-space observation of magnetic structures with well-defined topological properties and a lateral size as low as about 150 nm. They are generated in a thin ferrimagnetic film by ultrashort single optical laser…

Mesoscale and Nanoscale Physics · Physics 2013-05-06 M. Finazzi , M. Savoini , A. R. Khorsand , A. Tsukamoto , A. Itoh , L. Duò , A. Kirilyuk , Th. Rasing , M. Ezawa

Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions such as the autonomous…

Machine Learning · Computer Science 2023-07-05 Kohei Tsuchiyama , André Röhm , Takatomo Mihana , Ryoichi Horisaki , Makoto Naruse

Axisymmetric magnetic lines of nanometer sizes (chiral vortices or skyrmions) have been predicted to exist in a large group of noncentrosymmetric crystals more than two decades ago. Recently these magnetic textures have been directly…

Mesoscale and Nanoscale Physics · Physics 2016-08-14 N. S. Kiselev , A. N. Bogdanov , R. Schäfer , U. K. Rößler

In this work we propose a new route to describe topological excitations in magnetic systems through a single real scalar field. We show here that spherically symmetric structures in two spatial dimensions, which map helical excitations in…

Mesoscale and Nanoscale Physics · Physics 2016-05-06 D. Bazeia , M. M. Doria , E. I. B. Rodrigues

Non-Hermitian topological systems have attracted a lot of research activities in recent times, both theoretically and experimentally, due to their unique physical properties and association with open quantum systems. We show that modular…

Quantum Physics · Physics 2026-05-29 Saubhik Sarkar , Chiranjib Mukhopadhyay , Abolfazl Bayat

A superposition of spin helices can yield topological spin textures, such as skyrmion and hedgehog lattices. Based on the analogy with the moir\'e in optics, we study the magnetic and topological properties of such superpositions in a…

Strongly Correlated Electrons · Physics 2021-05-26 Kotaro Shimizu , Shun Okumura , Yasuyuki Kato , Yukitoshi Motome

Magnetic skyrmions are particle-like topological excitations recently discovered in chiral magnets. Their small size, topological protection and the ease with which they can be manipulated by electric currents generated much interest in…

Mesoscale and Nanoscale Physics · Physics 2017-04-05 A. O. Leonov , M. Mostovoy

Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single…

Quantum simulation with controllable many-body platforms offers a powerful route to exploring complex phases and dynamics that are difficult to access in natural materials. Among these, topological spin textures such as skyrmions are…

Thin-film sub-5 nm magnetic skyrmions constitute an ultimate scaling alternative for future digital data storage. Skyrmions are robust non-collinear spin-textures that can be moved and manipulated by small electrical currents. We show here…

Mesoscale and Nanoscale Physics · Physics 2015-10-20 Dax M. Crum , Mohammed Bouhassoune , Juba Bouaziz , Benedikt Schweflinghaus , Stefan Blügel , Samir Lounis

Reservoir computing is a relatively recent computational paradigm that originates from a recurrent neural network and is known for its wide range of implementations using different physical technologies. Large reservoirs are very hard to…

Reconfiguration has been used for both defect- and fault-tolerant nanoscale architectures with regular structure. Recent advances in self-assembled nanowires have opened doors to a new class of electronic devices with irregular structure.…

Emerging Technologies · Computer Science 2014-06-16 Alireza Goudarzi , Matthew R. Lakin , Darko Stefanovic , Christof Teuscher

Magnetic skyrmions are nanoscale topological spin structures offering great promise for next-generation information storage technologies. The recent discovery of sub-100 nm room temperature (RT) skyrmions in several multilayer films has…

Recent advancements in reservoir computing research have created a demand for analog devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy…

Reservoir computing is a recently introduced machine learning paradigm that has been shown to be well-suited for the processing of spatiotemporal data. Rather than training the network node connections and weights via backpropagation in…

Neural and Evolutionary Computing · Computer Science 2016-07-20 Ashley Prater
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