Thomas Schrefl

Univ.-Doz.Dipl.-Ing.Dr. Thomas Schrefl

Head of Center - Center for Modelling and Simulation

Projects (Extract Research Database)

Publications (Extract Research Database)

Heistracher, P.; Abert, C.; Bruckner, F.; Schrefl, T.; Suess, D. (2022). Proposal for a micromagnetic standard problem: domain wall pinning at phase boundaries. Journal of Magnetism and Magnetic Materials, Vol. 548: 168875

Mohapatra, J.; Fischbacher, J.; Gusenbauer, M.; Xing, M. Y.; Elkins, J.; Schrefl, T.; Liu, J. P. (2022). Coercivity limits in nanoscale ferromagnets. Phys. Rev. B, Vol. 105, Iss. 21: 214431

Oezelt, H.; Qu, L.; Kovacs, A.; Fischbacher, J.; Gusenbauer, M.; Beigelbeck, R.; Praetorius, D.; Yano, M.; Shoji, T.; Kato, A.; Chantrell, R.; Winklhofer, M.; Zimanyi, G.; Schrefl, T. (2022). Full- Spin-Wave-Scaled Stochastic Micromagnetism for Mesh-Independent Simulations of Ferromagnetic Resonance and Reversal. npj Computational Materials, Vol. 8: 35

Zhao, P.; Gusenbauer, M.; Oezelt, H.; Wolf, D.; Gemming, T.; Schrefl, T.; Nielsch, K.; Woodcock, T. G. (2022). Nanoscale chemical segregation to twin interfaces in t-MnAl-C and resulting effects on the magnetic properties. Journal of Materials Science & Technology, Vol. 134: 22-32

Cuadrado, R.; Evans, R. F. L.; Shoji, T.; Yano, M.; Kato, A.; Ito, M.; Hrkac, G.; Schrefl, T.; Chantrell, R.W. (2021). First principles and atomistic calculation of the magnetic anisotropy of Y2Fe14B. JOURNAL OF APPLIED PHYSICS, 130: 023901

Ener, S.; Skokov, K. P.; Palanisamy, D.; Devillers, T.; Fischbacher, J.; Eslavac, G.; Maccaria, F.; Schäfer, L.; Diop, L.; Radulov, I.; Gault, B.; Hrkac, G.; Dempsey, N.; Schrefl, T.;Raabe, D.; Gutfleisch, O. (2021). Twins – A weak link in the magnetic hardening of ThMn12-type permanent magnets. Acta Materialia, Vol. 214: 116968

Exl, L.; Mauser, N. J.; Schaffer, S.; Schrefl, T.; Suess, D.; (2021). Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method. JOURNAL OF COMPUTATIONAL PHYSICS, 444: 110586

Gusenbauer, M.; Kovacs, A.; Özelt, H.; Fischbacher, J.; Zhao, P.; Woodcock, T.G.;Schrefl, T. (2021). Insights into MnAl-C nano-twin defects by micromagnetic characterization. Journal of Applied Physics, 129(9): 093902

Perna, S.; Schrefl, T.; Serpico, C.; Fischbacher, J.; Del Pizzo, A. (2021). Microstructure Role in Permanent Magnet Eddy Current Losses. IEEE TRANSACTIONS ON MAGNETICS, 57: 6300405

Tsuchiura, H.; Yoshioka, T.; Novák, P.; Fischbacher, J.; Kovacs, A.; Schrefl, T. (2021). First-principles calculations of magnetic properties for analysis of magnetization processes in rare-earth permanent magnets". Science and Technology of Advanced Materials (STAM), Vol. 22, no. 1: 748-757

Exl, L.; Mauser, N.; Schrefl, T.; Suess, D. (2020). Learning time-stepping by nonlinear dimensionality reduction to predict magnetization dynamics. Communications in Nonlinear Science and Numerical Simulation, Vol. 84: 105205

Gusenbauer, G.; Oezelt, H.; Fischbacher, J.; Kovacs, A.; Zhao, P.; Woodcock, T. G.; Schrefl, T. (2020). Extracting local switching fields in permanent magnets using machine learning. npj Computational Materials, 6: 89ff

Kovacs, A.; Fischbacher, J.; Gusenbauer, M.; Oezelt, H.; Herper, H. C.; Vekilova, O. Y.; Nieves, P.; Arapan, S.; Schrefl, T. (2020). Computational design of rare-earth reduced permanent magnets. Engineering, 6: 148

Schönhöbel, A.M.; Madugundo, R.; Barandiarán, J.M.; Hadjipanayis, G.C.; Palanisamy, D.; Schwarz, T.; Gault, B.; Raabe, D.; Skokov, K.; Gutfleisch, O.; Fischbacher, J.; Schrefl, T. (2020). Nanocrystalline Sm-based 1:12 magnets. Acta Materialia, Vol. 200: 652-658

Skelland, C.; Westmoreland, S.C.; Ostler, T.; Evans, R.F.L.; Chantrell, R.W.; Yano, M.; Shoji, T.; Kato, A.; Ito, M.; Winklhofer, M.; Zimanyi, G.; Schrefl, T.; Fischbacher, J.; Hrkac, G. (2020). Atomistic study on the pressure dependence of the melting point of NdFe12. AIP Advances, Vol. 10, iss. 2: 025130

Tang, X.; Li, J.; Miyazaki, Y.; Sepehri-Amin, H.; Ohkubo, T.; Schrefl, T.; Hono, K. (2020). Relationship between the Thermal Stability of Coercivity and the Aspect Ratio of Grains in Nd-Fe-B Magnets: Experimental and Numerical Approaches. Acta Materialia, 183: 408-417

Westmoreland, S. C.; Skelland, C.; Shoji, T.; Yano, M.; Kato, A.; Ito, M.; Hrkac, G.; Schrefl, T.; Evans, R.; Chantrel, R. W. (2020). Atomistic simulations of a-Fe/Nd2Fe14B magnetic core/shell nanocomposites with enhanced energy product for high temperature permanent magnet applications. AIP, Vol. 127: 133901

Arapan, S.; Nieves, P.; Cuesta-López, S.; Gusenbauer, M.; Oezelt, H.; Schrefl, T.; Delczeg-Czirjak, E. K.; Herper, H. C.; Eriksson, O. (2019). Influence of antiphase boundary of the MnAl t-phase on the energy product. Physical Review Materials, Vol. 3, iss. 6: 064412

Dirba, I.; Li, J.; Sepehri-Amin, H.; Ohkubo, T.; Schrefl, T.; Hono, K. (2019). Single-Crystalline SmFe12-Based Microparticles with High Roundness Fabricated by Jet-Milling. Journal of Alloys and Compounds, 804: 155-162

Exl, L.; Fischbacher, J.; Kovacs, A.; Oezelt, H.; Gusenbauer, M.; Schrefl, T. (2019). Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization. Computer Physics Communications, 235: 179-186

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Lectures (Extract Research Database)

Inverse design of Nd-substituted permanent magnets

Physics and the green economy, 25/11/2021

Tutorial: An introduction to machine learning for solving micromagnetic problems

The 2021 Around-the-Clock Around-the-Globe Magnetics Conference, 24/08/2021

Deep learning magnetization dynamics

IEEE Advances in Magnetism 2021, 16/06/2021

New trends for machine learning in permanent magnet design

The 26th International Workshop on Rare Earth and Future Permanent Magnets and Their Application, 10/06/2021

Machine Learning for Relating Structure and Coercivity of Permanent Magnets

Virtual REPM 2021, 09/06/2021

Machine learning, micromagnetics and magnet design

University of York, Computational Magnetism, 02/12/2020

Finding weak spots in permanent magnets through micromagnetism and machine learning

CMD2020GEFES, 02/09/2020

Computational Design of Bulk Permanent Magnet

TMS2020, 25/02/2020

Bridging the gap between biomedical applications and material sciences

3rd Workshop on Modelling of Biological Cells, Fluid Flow and Microfluidics, 11/02/2020

Advancing permanent magnets by machine learning

Meeting of CRC/TRR 270 - Hysteresis design of magnetic materials for efficient energy conversion, 05/02/2020

Computer based optimization of permanent magnets

Seminar, CEA, Grenoble, 17/12/2019

Learning Magnetization Dynamics

64th Annual Conference on Magnetism and Magnetic Material, Las Vegas, USA, 07/11/2019

Machine learning for permanent magnet optimization

2019 - Sustainable Industrial Processing Summit & Exhibition, Paphos, Cryprus, 26/10/2019

Micromagnetic optimization of permanent magnetic materials

27th International Conference on Materials and Technology, Portoroz, Slovenia, 17/10/2019

Computational optimization of permanent magnets

Ruhr Symposium 2019, Duisburg, Germany, 09/10/2019

Modelling of microstructure for optimum hard magnetic properties

MMA’19: Magnetic Materials and Applications, Milano, Italy, 18/09/2019

Ferromagnetic resonance simulations for stochastic Landau-Lifshitz-Gilbert equation

The Joint European Magnetic Symposia (JEMS), Uppsala, Sweden, 29/08/2019

Micromagnetic characterization of MnAl-C using trained neural networks

JEMS2019, Uppsala, Schweden, 29/08/2019

Bridging the gap between academic software and industry needs - different business models for engaging with industry

EMMC Workshop, Cambridge, UK, 21/05/2019

Microstructure optimization for rare-earth efficient permanent magnets

DPG Spring Meeting, Regensburg, Germany, 01/04/2019

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