Thesis (Selection of subject)Thesis (Selection of subject)(version: 368)
Thesis details
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Proudem hnané skyrmiony
Thesis title in Czech: Proudem hnané skyrmiony
Thesis title in English: Current driven skyrmions
Key words: Skyrmiony|Transport|Nerovnováha|Neuromorfní počítání
English key words: Skyrmions|Transport|Non-equlibrium|Neuromorphic computing
Academic year of topic announcement: 2024/2025
Thesis type: diploma thesis
Thesis language:
Department: Department of Condensed Matter Physics (32-KFKL)
Supervisor: RNDr. Martin Žonda, Ph.D.
Author:
Guidelines
What will student learn:

Quantum classical equation of motion

Basics of nonequlibrium Green functions

Various Machine Learning techniques

What will student do:

Investigate transport in a layered magnetic system

Constructing models of skyrmionic devices for computing

Setting, running, and analyzing numerical simulations

Applying machine learning for phase classification and skyrmion tracking
References
Daniel C. Mattis, The Theory of Magnetism Made Simple
L. Peng et al., Dynamic transition of current-driven single-skyrmion motion in a room-temperature chiral-lattice magnet, Nature Communications 12, 6797 (2021)
A. Geron: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow:Concepts, Tools, and Techniques to Build Intelligent Systems, O’Reilly Media (2019)
Preliminary scope of work in English
Controlling skyrmion motion via electronic current is a promising technology potentially leading to skyrmion-based spintronic devices and with application in neuromorphic computing. Recent experimental progress allowed us to track and control skyrmions in some layered systems even at room temperatures, via short current pulses. Yet, a lot of theoretical problems remain unsolved, and this prohibits faster technological progress. The task of the student will be to address some of them. For example, the student will be searching for a way to move, place and stop skyrmions. This is important for building an analogue of artificial neural network in condensed matter device. In practice, this will mean to theoretically investigate a system that combines classical spins (Heisenberg vectors) with conducting electrons (quantum particles) driven out of equilibrium by external voltage drop or by a voltage pulse. The results, e.g., the phase diagrams and the movement of the skyrmion will be tracked by machine learning techniques.
 
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