index.json

[{"authors":null,"categories":null,"content":"Dr Matthew Ellis is a Lecturer in Machine Learning and member of the Machine Learning Group at the Department of Computer Science at the University of Sheffield. My research interests are in developing energy efficient devices based on spin-electronic devices and reservoir computing. I have previously worked with Prof. Stefano Sanvito at Trinity College Dublin and Prof. Roy Chantrell at the University of York.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"","publishdate":"0001-01-01T00:00:00Z","relpermalink":"","section":"authors","summary":"Dr Matthew Ellis is a Lecturer in Machine Learning and member of the Machine Learning Group at the Department of Computer Science at the University of Sheffield. My research interests are in developing energy efficient devices based on spin-electronic devices and reservoir computing.","tags":null,"title":"Matthew Ellis","type":"authors"},{"authors":[],"categories":null,"content":"","date":1641772800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1641772800,"objectID":"2772e76ed3e8d3ba8bec766a710c97b0","permalink":"https://mattoaellis.github.io/talk/joint-mmm-intermag-2022/","publishdate":"2021-09-30T09:51:10+01:00","relpermalink":"/talk/joint-mmm-intermag-2022/","section":"event","summary":"","tags":[],"title":"Joint MMM Intermag 2022","type":"event"},{"authors":["Matthew Ellis"],"categories":[],"content":"Due to the pandemic a couple of my projects ended up being a little later than planned so in the last few months we have had a couple more articles published on our neuromorphic and reservoir computing work. The first paper was published in Advanced Functional Materials on the emergent dynamics in a set of magnetic rings that we found could be used for reservoir computing. The experimental work was performed by researchers in the Department of Materials Science who manufactured and measured the properties of a grid of interconnected rings. The domain walls in these rings become pinned at the interfaces and can interact causing the emergent dynamics. As a reservoir it has a range of important properties (fading memory, non-linearity, etc) and we found excellent performance on a speech recognition task. The paper is available here.\nThe next paper was an invited paper in Applied Physics Letters for a special topic on Mesoscopic Magnetic Systems in which we introduced a novel reservoir based on thermally activated magnetic dots. While it might appear that the stochastic behaviour of these dots do not have much to offer, we found by applying a voltage controlled anisotropy we can tune the relaxation rates from one orientiation to another. Importantly this system is low energy so could find use in edge-computing systems where energy is limited. You can find the full article here.\nMost recently we have puclished our work on a non-linear domain wall oscillator as a physical reservoir. This was again in collaboration with Materials Science and we built a 1D model of the domain wall motion when it is pinned between two anti-notches. The pinning potential exhibits chaotic behaviour for fields upto 1.2 KA/m and a non-linear response beyond this. From this we could train it using the reservoir computing approach and found good performance on a set of tasks. You can read more here.\n","date":1630454400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1630454400,"objectID":"9fdf67edc83401de8a864a4a05701754","permalink":"https://mattoaellis.github.io/post/recent-articles/","publishdate":"2021-09-01T00:00:00Z","relpermalink":"/post/recent-articles/","section":"post","summary":"Due to the pandemic a couple of my projects ended up being a little later than planned so in the last few months we have had a couple more articles published on our neuromorphic and reservoir computing work.","tags":[],"title":"Recent Articles","type":"post"},{"authors":["Razvan V. Ababei","Matthew O. A. Ellis","Ian T. Vidamour","Dhilan S. Devadasan","Dan A. Allwood","Eleni Vasilaki","Thomas J. Hayward"],"categories":[],"content":"","date":1627862400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1627862400,"objectID":"a3a87e4f68c70580e36a54c161dc66b8","permalink":"https://mattoaellis.github.io/publication/ababei-2021/","publishdate":"2021-10-13T15:14:27+01:00","relpermalink":"/publication/ababei-2021/","section":"publication","summary":"Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.","tags":[],"title":"Neuromorphic computation with a single magnetic domain wall","type":"publication"},{"authors":["Alexander Welbourne","Axel L. R. Levy","Matthew O. A. Ellis","H. Chen","M. Thompson","Eleni Vasilaki","Dan. A. Allwood","Thomas j. Hayward"],"categories":[],"content":"","date":1621262319,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1621262319,"objectID":"c5510c099fa313766791513debcfd77a","permalink":"https://mattoaellis.github.io/publication/welbourne-2021/","publishdate":"2021-09-29T15:38:39+01:00","relpermalink":"/publication/welbourne-2021/","section":"publication","summary":"We propose thermally driven, voltage-controlled superparamagnetic ensembles as low-energy platforms for hardware-based reservoir computing. In the proposed devices, thermal noise is used to drive the ensembles' magnetization dynamics, while control of their net magnetization states is provided by strain-mediated voltage inputs. Using an ensemble of CoFeB nanodots as an example, we use analytical models and micromagnetic simulations to demonstrate how such a device can function as a reservoir and perform two benchmark machine learning tasks (spoken digit recognition and chaotic time series prediction) with competitive performance. Our results indicate robust performance on timescales from microseconds to milliseconds, potentially allowing such a reservoir to be tuned to perform a wide range of real-time tasks, from decision making in driverless cars (fast) to speech recognition (slow). The low energy consumption expected for such a device makes it an ideal candidate for use in edge computing applications that require low latency and power.","tags":["Reservoir Computing","Low Power","Speech Recognition","Neuromorphic"],"title":"Voltage-controlled superparamagnetic ensembles for low-power reservoir computing","type":"publication"},{"authors":["Richard W. Dawidek","Thomas J. Hayward","Ian T. Vidamour","Thomas J. Broomhall","Guru Venkat","Mohanad Al Mamoori","Aidan Mullen","Stephan J. Kyle","Paul W. Fry","Nina-Juliane Steinke","Joshaniel F. K. Cooper","Francesco Maccherozzi","Sarnjeet S. Dhesi","Lucia Aballe","Michael Foerster","Jordi Prat","Eleni Vasilaki","Matthew O. A. Ellis","Dan A. Allwood"],"categories":[],"content":"","date":1614205818,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1614205818,"objectID":"404b444b8733c891807948569176f7b3","permalink":"https://mattoaellis.github.io/publication/dawidek-2021/","publishdate":"2021-02-24T22:30:18Z","relpermalink":"/publication/dawidek-2021/","section":"publication","summary":"Emergent behaviors occur when simple interactions between a system's constituent elements produce properties that the individual elements do not exhibit in isolation. This article reports tunable emergent behaviors observed in domain wall (DW) populations of arrays of interconnected magnetic ring-shaped nanowires under an applied rotating magnetic field. DWs interact stochastically at ring junctions to create mechanisms of DW population loss and gain. These combine to give a dynamic, field-dependent equilibrium DW population that is a robust and emergent property of the array, despite highly varied local magnetic configurations. The magnetic ring arrays’ properties (e.g., non-linear behavior, “fading memory” to changes in field, fabrication repeatability, and scalability) suggest they are an interesting candidate system for realizing reservoir computing (RC), a form of neuromorphic computing, in hardware. By way of example, simulations of ring arrays performing RC approaches 100% success in classifying spoken digits for single speakers.","tags":["Machine Learning","Reservoir Computing"],"title":"Dynamically-Driven Emergence in a Nanomagnetic System","type":"publication"},{"authors":["Luca Manneschi","Matthew O. A. Ellis","Guido Gigante","Andrew C. Lin","Paolo Del Guidice","Eleni Vasilaki"],"categories":[],"content":"","date":1613572181,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1613572181,"objectID":"a7b2d11abd91276cb3df6cb6b68bca07","permalink":"https://mattoaellis.github.io/publication/manneschi-2021/","publishdate":"2021-09-29T15:29:41+01:00","relpermalink":"/publication/manneschi-2021/","section":"publication","summary":"Echo state networks (ESNs) are a powerful form of reservoir computing that only require training of linear output weights while the internal reservoir is formed of fixed randomly connected neurons. With a correctly scaled connectivity matrix, the neurons’ activity exhibits the echo-state property and responds to the input dynamics with certain timescales. Tuning the timescales of the network can be necessary for treating certain tasks, and some environments require multiple timescales for an efficient representation. Here we explore the timescales in hierarchical ESNs, where the reservoir is partitioned into two smaller linked reservoirs with distinct properties. Over three different tasks (NARMA10, a reconstruction task in a volatile environment, and psMNIST), we show that by selecting the hyper-parameters of each partition such that they focus on different timescales, we achieve a significant performance improvement over a single ESN. Through a linear analysis, and under the assumption that the timescales of the first partition are much shorter than the second’s (typically corresponding to optimal operating conditions), we interpret the feedforward coupling of the partitions in terms of an effective representation of the input signal, provided by the first partition to the second, whereby the instantaneous input signal is expanded into a weighted combination of its time derivatives. Furthermore, we propose a data-driven approach to optimise the hyper-parameters through a gradient descent optimisation method that is an online approximation of backpropagation through time. We demonstrate the application of the online learning rule across all the tasks considered.","tags":["Reservoir Computing","Echo State Networks","Hyper-parameter Optimisation"],"title":"Exploiting Multiple Timescales in Hierarchical Echo State Networks","type":"publication"},{"authors":["Mara Strungaru","Matthew O. A. Ellis","Sergiu Ruta","Oksana Chubykalo-Fesenko","Richard F. L. Evans","Roy W. Chantrell"],"categories":[],"content":"","date":1611051860,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611051860,"objectID":"636fd7210fab310d9d3f11ccf4122a7c","permalink":"https://mattoaellis.github.io/publication/strungaru-2021/","publishdate":"2021-01-27T10:24:20Z","relpermalink":"/publication/strungaru-2021/","section":"publication","summary":"A unified model of molecular and atomistic spin dynamics is presented enabling simulations both in microcanonical and canonical ensembles without the necessity of additional phenomenological spin damping. Transfer of energy and angular momentum between the lattice and the spin systems is achieved by a phenomenological coupling term representing the spin-orbit interaction.","tags":["Atomistic Spin Dynamics","Mageto-elastic Coupling","Gilbert Damping"],"title":"Spin-lattice dynamics model with angular momentum transfer for canonical and microcanonical ensembles","type":"publication"},{"authors":[],"categories":[],"content":"It was nice early Christmas present that we heard that two of our recent submissions had been accepted. The first was invited article in Frontiers in Applied Mathematics and Statistics where we have been looking at hierarchical (or deep) echo state networks and how we can tune them to exhibit multiple timescales. The abstract is available on the Frontiers webpage and the full article should be available soon.\nThe second was a collaboration with the University of York that has been ongoing since my PhD. This is developing a combined model of spin and moelcular dynamics which are simulated concurrently. A few models of spin-lattice dynamics have been proposed already but here we present some novel results of the Gilbert damping caused by the coupling of the spin system to lattice. This is a very interesting topic and we will be using our model to explore spin-lattice dynamics in the future. Whilst the article is not yet out at Physical Review B, the pre-print can be found on Arxiv here.\n","date":1610356563,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1610356563,"objectID":"22b1c61b9fd69017203d09c7c04db412","permalink":"https://mattoaellis.github.io/post/upcoming-papers/","publishdate":"2021-01-11T09:16:03Z","relpermalink":"/post/upcoming-papers/","section":"post","summary":"It was nice early Christmas present that we heard that two of our recent submissions had been accepted. The first was invited article in Frontiers in Applied Mathematics and Statistics where we have been looking at hierarchical (or deep) echo state networks and how we can tune them to exhibit multiple timescales.","tags":["Atomistic Spin Dynamics","Echo State Networks"],"title":"Upcoming Papers","type":"post"},{"authors":[],"categories":[],"content":"Happy new year everyone!\nMoving into 2021 I have started a small refresh of this website. I have updated the publications and added a new post about our upcoming papers. 2020 was a strange year of lockdowns and lots of working from home, 2021 is looking similar so far but fingers crossed for a productive and interesting year.\n","date":1609951633,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951633,"objectID":"194e9745e31d54cb5ed2ade4ac9dabdf","permalink":"https://mattoaellis.github.io/post/new-year/","publishdate":"2021-01-06T16:47:13Z","relpermalink":"/post/new-year/","section":"post","summary":"Happy new year everyone!\nMoving into 2021 I have started a small refresh of this website. I have updated the publications and added a new post about our upcoming papers. 2020 was a strange year of lockdowns and lots of working from home, 2021 is looking similar so far but fingers crossed for a productive and interesting year.","tags":[],"title":"New Year","type":"post"},{"authors":[],"categories":null,"content":"","date":1607437800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1607437800,"objectID":"311e4fd5b3063d8b6db104d9e25465c6","permalink":"https://mattoaellis.github.io/talk/stochastic-domain-wall-dynamics-for-machine-learning/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/stochastic-domain-wall-dynamics-for-machine-learning/","section":"event","summary":"Harnessing stochasic DW dynamics for neuromorphic computing","tags":[],"title":"Stochastic Domain Wall Dynamics for Machine Learning","type":"event"},{"authors":["Matthew O A Ellis","Mario Galante","Stefano Sanvito"],"categories":[],"content":"","date":1575158400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951340,"objectID":"de70da5064a28503624b83a716765abf","permalink":"https://mattoaellis.github.io/publication/ellis-2019/","publishdate":"2021-01-06T16:42:19.960298Z","relpermalink":"/publication/ellis-2019/","section":"publication","summary":"L10 FePt is a technologically important material for a range of novel data storage applications. In the ordered FePt structure the normally nonmagnetic Pt ion acquires a magnetic moment, which depends on the local field originating from the neighboring Fe atoms. In this work a model of FePt is constructed in which the induced Pt moment is simulated by using combined longitudinal and rotational spin dynamics. The model is parameterized to include a linear variation of the moment with the exchange field, so that at the Pt site the magnetic moment depends on the Fe ordering. The Curie temperature of FePt is calculated and agrees well with similar models that incorporate the Pt dynamics through an effective Fe-only Hamiltonian. By computing the dynamic correlation function the anisotropy field and the Gilbert damping are extracted over a range of temperatures. The anisotropy exhibits a power-law dependence on the magnetization with exponent n≈2.1. This agrees well with what was observed experimentally, and it is obtained without including a two-ion anisotropy term as in other approaches. Our work shows that incorporating longitudinal fluctuations into spin dynamics calculations is crucial for understanding the properties of materials with induced moments.","tags":["FePt","Atomistic Spin Dynamics","Longituindal Dynamics","Ferromagnetic Resonance"],"title":"Role of longitudinal fluctuations in L10 FePt","type":"publication"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Wowchemy Wowchemy | Documentation\n Features  Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides   Controls  Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E   Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;)   Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\n Fragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}}  Press Space to play!\nOne  Two  Three \n A fragment can accept two optional parameters:\n class: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears   Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}}  Press the S key to view the speaker notes!\n Only the speaker can read these notes Press S key to view    Themes  black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links    night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links   Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/media/boards.jpg\u0026quot; \u0026gt;}} {{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}} {{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}}   Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; }   Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://mattoaellis.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Wowchemy's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":["Razvan-V. Ababei","Matthew O. A. Ellis","Richard F. L. Evans","Roy W. Chantrell"],"categories":[],"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951340,"objectID":"f68c5e91c665d8f22b602876f229a30e","permalink":"https://mattoaellis.github.io/publication/ababei-2019/","publishdate":"2021-01-06T16:42:20.101778Z","relpermalink":"/publication/ababei-2019/","section":"publication","summary":"","tags":["Atomistic Spin Dynamics"],"title":"Anomalous damping dependence of the switching time in Fe/FePt bilayer recording media","type":"publication"},{"authors":["Mario Galante","Matthew O. A. Ellis","Stefano Sanvito"],"categories":[],"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951340,"objectID":"ab98b15eca351ed6f28f44384a04cd9b","permalink":"https://mattoaellis.github.io/publication/galante-2019/","publishdate":"2021-01-06T16:42:20.238628Z","relpermalink":"/publication/galante-2019/","section":"publication","summary":"","tags":["FePt","Magnetic tunnel junctions","Ab initio"],"title":"Nontrivial spatial dependence of the spin torques in L10 FePt-based tunneling junctions","type":"publication"},{"authors":["Matthew O. A. Ellis","Maria Stamenova","Stefano Sanvito"],"categories":[],"content":"","date":1512086400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951341,"objectID":"687658242cdbe803dcc6876a589e9634","permalink":"https://mattoaellis.github.io/publication/ellis-2017-b/","publishdate":"2021-01-06T16:42:21.167433Z","relpermalink":"/publication/ellis-2017-b/","section":"publication","summary":"","tags":["Multiscale modelling","Magnetic tunnel junctions","Ab initio"],"title":"Multiscale modeling of current-induced switching in magnetic tunnel junctions using ab initio spin-transfer torques","type":"publication"},{"authors":["Matthew O A Ellis","Razvan-v Ababei","Roger Wood","Richard F L Evans","Roy W Chantrell"],"categories":[],"content":"","date":1501545600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951340,"objectID":"b611302ab0ba123f7c730321a7d25e91","permalink":"https://mattoaellis.github.io/publication/ellis-2017-a/","publishdate":"2021-01-06T16:42:20.498711Z","relpermalink":"/publication/ellis-2017-a/","section":"publication","summary":"","tags":["FePt","Granular recording media"],"title":"Manifestation of higher-order inter-granular exchange in magnetic recording media","type":"publication"},{"authors":["Matthew O. A. Ellis","Eric E. Fullerton","Roy W. Chantrell"],"categories":[],"content":"","date":1467331200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951341,"objectID":"c2c3242eac62e64b4a9a338cb4fad96b","permalink":"https://mattoaellis.github.io/publication/ellis-2016/","publishdate":"2021-01-06T16:42:21.300202Z","relpermalink":"/publication/ellis-2016/","section":"publication","summary":"Magnetic recording using circularly polarized femto-second laser pulses is an emerging technology that would allow write speeds much faster than existing field driven methods. However, the mechanism that drives the magnetization switching in ferromagnets is unclear. Recent theories suggest that the interaction of the light with the magnetized media induces an opto-magnetic field within the media, known as the inverse Faraday effect. Here we show that an alternative mechanism, driven by thermal excitation over the anisotropy energy barrier and a difference in the energy absorption depending on polarization, can create a net magnetization over a series of laser pulses in an ensemble of single domain grains. Only a small difference in the absorption is required to reach magnetization levels observed experimentally and the model does not preclude the role of the inverse Faraday effect but removes the necessity that the opto-magnetic field is 10s of Tesla in strength.","tags":["All optical switching","Granular recording media"],"title":"All-optical switching in granular ferromagnets caused by magnetic circular dichroism","type":"publication"},{"authors":null,"categories":null,"content":"Machine learning, or the ability of computers to intelligently analyse complex data sets, may be the defining technology of our age. Society produces huge amounts of data in areas as diverse as economics, weather, medicine, science and social media, analysis of which can greatly enhance our decision making. However, current computers are poorly suited to analysing large and complex datasets, particularly when compared to animal and human brains.\nFor example, while a typical contemporary computer can perform simple numerical calculations much more quickly than the brain, its efficiency is almost one million times lower when performing more complex data analysis such as recognising human faces. This inefficiency results from attempts to emulate \u0026ldquo;neuromorphic\u0026rdquo;, or brain-like, computational processes by brute force on hardware which is ill-suited to the task. For example, in a conventional computer memory and processing are inherently separated, whereas they share the same medium in the brain. To overcome these limitations, bespoke computers that are specifically designed for neuromorphic computation are required.\nAway from pure computational power, the \u0026ldquo;big data\u0026rdquo; revolution has been driven by our ability to store information. This is the result of nano-scale magnetic technology in the form of hard-disk drives. However, nanomagnetic devices suffer from an encroaching limitation, in that their behaviour becomes increasingly unreliable, or stochastic, as they are further miniaturised to increase performance. While this is devastating for conventional digital computers, there is strong evidence that stochastic behaviour can be tolerated in, or even enhance, the performance of neuromorphic technologies. This raises the tantalising prospect of nanomagnetic technology being ideally suited for developing new computer forms of computer hardware for data analysis.\nIn this project, a collaboration between experts in nanomagnetic technology and computer science at the University of Sheffield, we will perform a pilot study to investigate whether stochastic behaviour in \u0026ldquo;magnetic domain wall devices\u0026rdquo; a promising form of magnetic nanotechnology where magnetic information is flowed through nanowire conduits, can be used to realise new, neuromorphic computer architectures. Working with an advisory board of academic and industry experts in machine learning technology we will demonstrate new data analysis devices, before creating a roadmap to develop the devices into real technology. Eventual success in developing these technologies could result in powerful hardware platforms that can provide even the smallest device the ability to intelligently analyse its environment to make informed decisions.\nCredit: Tom Hayward, Dan Allwood and Eleni Vasilaki\n","date":1461715200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1461715200,"objectID":"3ef1a62c271996106f7bd902820f0fcf","permalink":"https://mattoaellis.github.io/project/stochastics/","publishdate":"2016-04-27T00:00:00Z","relpermalink":"/project/stochastics/","section":"project","summary":"Machine learning with magnetic hardware","tags":["Machine Learning"],"title":"From Stochasticity to Functionality","type":"project"},{"authors":["Matthew O. A. Ellis","Roy W. Chantrell"],"categories":[],"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951340,"objectID":"d23d294220c0bbba956bf1bc9eefaef7","permalink":"https://mattoaellis.github.io/publication/ellis-2015-fe-pt/","publishdate":"2021-01-06T16:42:20.764654Z","relpermalink":"/publication/ellis-2015-fe-pt/","section":"publication","summary":"","tags":["FePt","Granular Recording Media"],"title":"Switching times of nanoscale FePt: Finite size effects on the linear reversal mechanism","type":"publication"},{"authors":["M. O. A. Ellis","R. F. L. Evans","T. A. Ostler","J. Barker","U. Atxitia","O. Chubykalo-Fesenko","R. W. Chantrell"],"categories":[],"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951340,"objectID":"4fe63759fcc8f0c8f22f6fee624fe6b1","permalink":"https://mattoaellis.github.io/publication/ellis-2015-ll/","publishdate":"2021-01-06T16:42:20.36856Z","relpermalink":"/publication/ellis-2015-ll/","section":"publication","summary":"","tags":["atomistic spin models","Landau Lifshitz equation"],"title":"The Landau–Lifshitz equation in atomistic models","type":"publication"},{"authors":["Thomas A Ostler","Matthew O A Ellis","Denise Hinzke","Ulrich Nowak"],"categories":[],"content":"","date":1409529600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951340,"objectID":"2c00191c13e7dbece9c3d5f6f71c63ce","permalink":"https://mattoaellis.github.io/publication/ostler-2014/","publishdate":"2021-01-06T16:42:20.632269Z","relpermalink":"/publication/ostler-2014/","section":"publication","summary":"Using the Landau-Lifshitz-Bloch (LLB) equation for ferromagnetic materials, we derive analytic expressions for temperature dependent absorption spectra as probed by ferromagnetic resonance (FMR). By analysing the resulting expressions, we can predict the variation of the resonance frequency and damping with temperature and coupling to the thermal bath. We base our calculations on the technologically relevant L1$_0$ FePt, parameterised from atomistic spin dynamics simulations, with the Hamiltonian mapped from ab-initio parameters. By constructing a multi-macrospin model based on the LLB equation and exploiting GPU acceleration we extend the study to investigate the effects on the damping and resonance frequency in $backslashmu$m sized structures.","tags":["FePt","Landau-Lifshitz-Bloch","Ferromagnetic resonance"],"title":"Temperature-dependent ferromagnetic resonance via the Landau-Lifshitz-Bloch equation: Application to FePt","type":"publication"},{"authors":["R F L Evans","W J Fan","P Chureemart","T. A. Ostler","M O A Ellis","R W Chantrell"],"categories":[],"content":"","date":1393632000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951341,"objectID":"348bf12ed2a4343389d66f73db4d0ca6","permalink":"https://mattoaellis.github.io/publication/evans-2014/","publishdate":"2021-01-06T16:42:20.894304Z","relpermalink":"/publication/evans-2014/","section":"publication","summary":"Atomistic modelling of magnetic materials provides unprecedented detail about the underlying physical processes that govern their macroscopic properties, and allows the simulation of complex effects such as surface anisotropy, ultrafast laser-induced spin dynamics, exchange bias, and microstructural effects. Here we present the key methods used in atomistic spin models which are then applied to a range of magnetic problems. We detail the parallelization strategies used which enable the routine simulation of extended systems with full atomistic resolution.","tags":["atomistic model","classical spin model","magnetism","monte carlo","spin dynamics"],"title":"Atomistic spin model simulations of magnetic nanomaterials","type":"publication"},{"authors":["Matthew O. A. Ellis","Thomas A. Ostler","Roy W. Chantrell"],"categories":[],"content":"","date":1351728000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609951341,"objectID":"5865fd9b4481ef3a7f67f9c86703bef4","permalink":"https://mattoaellis.github.io/publication/ellis-2012/","publishdate":"2021-01-06T16:42:21.031525Z","relpermalink":"/publication/ellis-2012/","section":"publication","summary":"","tags":["Atomistic Spin Dynamics","Rare Earths","Gilbert Damping"],"title":"Classical spin model of the relaxation dynamics of rare-earth doped permalloy","type":"publication"}]