Permanent magnets are crucial components in sustainable technologies such as electric vehicles and wind turbines. However, the production of high-performance magnets currently relies heavily on rare earth elements, which could lead to a shortage due to the increasing demand for these materials. To address this issue, alternative magnets with significantly lower rare earth content are needed.
One promising solution is a two-phase magnet that incorporates both hard and soft magnetic regions. The goal of this project is to find the optimal spatial distribution for these regions to reduce rare earth content while maintaining high magnetic performance. The project will employ fast, massively parallel micromagnetic simulations and artificial intelligence techniques.
A framework will be developed to automatically prepare and perform a large number simulations for various hard/soft magnetic distributions. The results will be used as training data for a neural network called the Predictor. The Predictor will learn the influence of material composition and geometrical properties on the performance of the magnet. The trained network will then be used inversely as a Designer network to optimize the material composition and geometrical properties. In an active learning scheme the predicted designs will be validated by micromagnetic simulations and fed back into the neural network as training data.
Ultimately, the generative neural network for inverse design of high-performance, rare earth reduced permanent magnets will provide new guidelines for producing eco-friendly permanent magnets for sustainable technologies.
On September 22nd 2023, we gave multiple workshops for school classes in the framework of the Forschungsfest Niederösterreich 2023. In the admission-free workshops called “Magnetism in Motion: Hands-on Electric Motor Workshop” students could not only learn about the role of strong magnets in electric motors and generators, but also build their own simple electric motor.
On September 13th, 2023, we kindly invite you to a IEEE Distinguished Lecture 2023 by J. Ping Liu. He is an IEEE Fellow at the University of Texas at Arlington, USA and will talk about Magnetic Hardening in Low-Dimensional Ferromagnets.
On July 17th, 2023, an interview with Harald Özelt was published on scilog, the magazine of the Austrian Science Fund FWF. The article delves into the application of artificial intelligence to optimize strong magnets for the energy transition. Harald elaborates on the team's goal to reduce reliance on rare earth elements while enhancing magnet performance for electric motors and generators. The interview highlights Harald's two projects, DeNaMML and DataMag, which explore the nanostructure of individual magnetic grains and their interactions in magnetic systems with various chemical compositions. The article garnered attention from other newspapers and platforms such as ORF, APA, Die Presse, Bild, Jungfrauzeitung, Studium.at, Salzburger Nachrichten, Nau.ch, resulting in various versions being published. The research team extends its gratitude to the Austrian Science Fund for providing a platform to showcase their research.
From June 19th to June 23rd, 2023, Heisam Moustafa attended the 16th Madrid UPM Machine Learning and Advanced Statistics Summer School (MLAS). He participated in the courses 'Bayesian Networks' and 'Neural Networks and Deep Learning'. Both courses aimed to deepen knowledge in these areas through theory and practical examples. This knowledge will be applied in the development of machine learning methods for magnet design.
From June 4th to June 7th, 2023, the 13th International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2023) took place. Harald Özelt chaired the session on "Machine learning" and presented the current state of the project to the participants through a poster. It was explained how a Convolutional Neural Network was generated and trained with simulation data. The next step demonstrated that this model could be used in an optimization loop to find better distributions of magnetically hard and soft phases. As the project partners from the MMM Platform of University of Vienna, Lukas Exl and Sebastian Schaffer, also attended the conference, the evening hours were used for extensive project discussions.
H. Oezelt, et al., "Machine learning based optimization of hard-/soft magnetic nanostructures", 13th International Symposium on Hysteresis Modeling and Micromagnetics, Vienna, Austria. Poster presentation, June 2023, doi: 10.13140/RG.2.2.17787.49445.
From May 15 to May 19, 2023, the IEEE International Magnetics Conference INTERMAG 2023 took place in Sendai, Japan. Under the title "Convolutional neural networks to predict properties of magnetic nanostructures," Harald Özelt presented the latest project results. In his presentation, he explained how a Convolutional Neural Network (CNN) was trained using data from micromagnetic simulations of hard and soft magnetic phase distributions. While the simulations can take several hours to calculate properties such as coercive field, remanence, or energy density product for permanent magnets with specific phase distributions, the neural network is able to predict these properties within seconds.
In mid-April 2023, Heisam Moustafa joined our center as a PhD student, focusing on micromagnetic simulations, reduced-order models, and neural networks. Prior to joining us, Heisam completed his Master of Science in Space Engineering at the University of Bremen and worked at ZARM TECHNIK AG in the field of magnetism and structural design.
In April 2023, an article was published in the Journal of Magnetism and Magnetic Materials, introducing physics-informed neural networks (PINN) for micromagnetic equations. The article explores higher-order optimization methods for training these networks. The focus was on reducing the required computational resources by providing additional physical parameters to the network through single low-parametric neural networks. The article is available as an open-access publication, allowing unrestricted access for all readers.
S. Schaffer et al., “Physics-informed machine learning and stray field computation with application to micromagnetic energy minimization,” Journal of Magnetism and Magnetic Materials, vol. 576, p. 170761, Jun. 2023, doi: 10.1016/j.jmmm.2023.170761.
On April 11, 2023, an article titled "Magnets for the Energy Transition" was published in the magazine for knowledge and thinking ahead by the University for Continuing Education Krems called "upgrade." In the interview, Harald Özelt talks about the importance of researching magnetic materials, as well as his career path and research routine. The article can be read in Issue 1.23 with a focus on Simulating & Measuring, starting from page 46.
On November 17, 2022, the kickoff meeting took place at the project partner's location at the University of Vienna. The participants included Lukas Exl and Sebastian Schaffer from the MMM Platform, as well as Harald Özelt and Thomas Schrefl from UWK. The initial steps for creating and exchanging training data were established. Advantageous implementations of neural networks and frameworks were discussed..
On July 13, 2022, as part of the Jungen Uni at the Krems Campus, a workshop was held for children between the ages of 10 and 13. After a lecture on permanent magnets and electric motors, the participants had the opportunity to create a simple electric motor themselves. In another short presentation, it was demonstrated how the performance of their motors could be improved through simple simulation techniques.
Already before the start of the project, the importance of strong permanent magnets for future green technologies and the energy transition was communicated to the public during the Langen Nacht der Forschung 2022, which took place on May 20th. At an interactive station, visitors had the opportunity to experience through simple experiments how the performance of electric motors can be influenced by powerful NdFeB magnets. The accompanying discussions and poster presentation addressed the role of critical elements and how simulation and machine learning can help reduce them.