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

Donau-Universität Krems

Thomas Schrefl

Projekte (Auszug Forschungs­datenbank)

Publikationen (Auszug Forschungs­datenbank)

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, elsevier

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, Springer Nature

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, Elsevier

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, Elsevier

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, AIP

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, elsevier

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, AIP

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

Gusenbauer, M.; Fischbacher, J.; Kovacs, A.; Oezelt, H.; Bance, S.; Zhao, P.; Woodcock, T.G.; Schrefl, T. (2019). Automated meshing of electron backscatter diffraction data and application to finite element micromagnetics. Journal of Magnetism and Magnetic Materials, Volume 486: 165256

Kovacs, A.; Fischbacher, J.; Gusenbauer, M.; Oezelt, H.; Herper, H. C.; Vekilova, O. Yu.; Nieves, P.; Arapan, S.; Schrefl, T. (2019). Computational Design of Rare-Earth Reduced Permanent Magnets. Engineering, November 2019: in press

Kovacs, A.; Fischbacher, J.; Oezelt, H.; Gusenbauer, M.; Exl, L.; Bruckner, F.; Suess, D.; Schrefl, T. (2019). Learning Magnetization Dynamics. Journal of Magnetism and Magnetic Materials, 491: 165548

Nieves, P.; Arapan, S.; Maudes-Raedo, J.; Marticorena-Sánchez, R.; Del Brío, N. L.; Kovacs, A.; Echevarria-Bonet, C.; Salazar, D.; Weischenberg, J.; Zhang, H.; Vekilova, O. Yu.; Serrano-López, R.; Barandiaran, J. M.; Skokov, K.; Gutfleisch, O.; Eriksson, O.; Herper, H. C.; Schrefl, T.; Cuesta-López, S. (2019). Database of Novel Magnetic Materials for High-Performance Permanent Magnet Development. Computational Materials Science, 168: 188-202

Sepehri-Amin, H.; Dirba, I.; Tang, X.; Ohkubo, T.; Schrefl, T.; Gutfleisch, O.; Hono, K. (2019). Development of High Coercivity Anisotropic Nd-Fe-B/Fe Nanocomposite Powder Using Hydrogenation Disproportionation Desorption Recombination Process. Acta Materialia, 175: 276-285

Skelland, C.; Ostler, T.; Westmoreland, S.C.; Evans, R.F.L.; Chantrell, R.W.; Yano, M.; Shoji, T.; Kato, A.; Winkelhofer, M., Zimanyi, G.; Fischbacher, J.; Schrefl, T.; Hrkac, G. (2019). The Effect of Interstitial Nitrogen Addition on the Structural Properties of Supercells of NdFe12-xTix. IEEE Transactions on Magnetics, Vol. 55, iss. 10: 6700205

Soderznik, M.; Li, J.; Liu, L.; Sepehri-Amin, H.; Ohkubo, T.; Sakuma, N.; Shoji, T.; Kato, A.; Schrefl, T.; Hono, K. (2019). Magnetization reversal process of anisotropic hot-deformed magnets observed by magneto-optical Kerr effect microscopy. Journal of Alloys and Compounds, 771: 51/https://doi.org/10.1016/j.jallcom.2018.08.231

Vekilova, O. Y.; Fayyazi, B.; Skokov, K. P.; Gutfleisch, O.; Echevarria-Bonet, C.; Barandiarán, J. M.; Kovacs, A.; Fischbacher, J.; Schrefl, T.; Eriksson, O.; Herper, H. C. (2019). Tuning the Magnetocrystalline Anisotropy of Fe3Sn by Alloying. Physical Review B, 99: 024421

Exl, L.; Fischbacher, J.; Kovacs, A.; Oezelt, H.; Gusenbauer, M.; Yokota, K.; Shoji, T., Hrkac, G.; Schrefl, T.; (2018). Magnetic microstructure machine learning analysis. JPhys Materials, 2: 014001/https://doi.org/10.1088/2515-7639/aaf26d

Fischbacher, J.; Kovacs, A.; Exl, L.; Kühnel, J.; Mehofer, E.; Sepehri-Amin, H.; Ohkubo, T.; Hono, K.; Schrefl, T. (2018). Searching the weakest link: Demagnetizing fields and magnetization reversal in permanent magnets. Scripta Materialia, 154: 253/https://doi.org/10.1016/j.scriptamat.2017.11.0

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Vorträge (Auszug Forschungs­datenbank)

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

Micromagnetic characterization of MnAl-C using trained neural networks

JEMS2019, Uppsala, Schweden, 29.08.2019

Ferromagnetic resonance simulations for stochastic Landau-Lifshitz-Gilbert equation

The Joint European Magnetic Symposia (JEMS), Uppsala, Sweden, 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

Magnetic materials modelling – Bridging the gap between academic software and industry needs

EMMC International Workshop 2019, Vienna, Austria, 27.02.2019

Simulation of permanent magnets across the length scales

Functional Materials Colloquium, TU Darmstadt, 26.10.2018

Automated micromagnetic simulations from Electron Backscatter Diffraction data

JEMS 2018, 05.09.2018

Computational design of rare-earth reduced permanent magnets

Rare-earth and future permanent magnets and their applications REPM2018, Beijing, China, 28.08.2018

Energy Barriers in Nano-structured Permanent Magnets

Conference on Mathematical Aspects of Materials Science, Portland Oregon, USA, 10.07.2018

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