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Simultaneous Transcranial Alternating Current Stimulation and Functional Magnetic Resonance Imaging

Simultaneous Transcranial Alternating Current Stimulation and Functional Magnetic Resonance Imaging

Published on: June 5, 2017

Simulating functional magnetic materials on supercomputers.

Markus Ernst Gruner¹, Peter Entel

¹Physics Department and Centre for Nanointegration CENIDE, University of Duisburg-Essen, Duisburg Campus, 47048 Duisburg, Germany.

Journal of Physics. Condensed Matter : an Institute of Physics Journal

|August 11, 2011

Summary

This summary is machine-generated.

High-performance computing enables large-scale simulations of materials. Density functional theory calculations reveal insights into magnetic nanoparticles for data storage and magnetic shape memory alloys for actuators.

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Area of Science:

Computational Materials Science
Condensed Matter Physics
Nanotechnology

Background:

Advancements in high-performance scientific computing, including petaflop-scale systems, facilitate complex simulations.
Supercomputers enable the investigation of large systems, such as thousands of spin-polarized transition metal atoms using density functional theory.

Purpose of the Study:

To present applications of large-scale ab initio calculations in understanding structure-magnetism relationships.
To investigate the size-dependent structural evolution of iron, Fe-Pt, and Co-Pt nanoparticles for magnetic data storage.
To explore the magnetic shape memory effect in Ni-Mn-Ga Heusler alloys for magnetomechanical applications.

Main Methods:

Large-scale ab initio calculations using density functional theory (DFT).
Investigation of equilibrium structural motifs in transition metal nanoparticles.
Analysis of magnetic properties and phase diagrams in relevant alloys.

Main Results:

For Fe-Pt and Co-Pt nanoparticles, multiply twinned morphologies at smaller sizes hinder the formation of the L1(0) phase required for hard magnetic properties.
The magnetic shape memory effect in Ni-Mn-Ga alloys is linked to field-induced martensitic twin boundary shifting.
High mobility of martensitic twin boundaries and selection of appropriate martensitic structures are crucial for the magnetic shape memory effect.

Conclusions:

Large-scale DFT calculations are vital for understanding complex material properties arising from structure-magnetism interplay.
Challenges in achieving desired magnetic properties for data storage in nanoparticles due to structural preferences.
The magnetic shape memory effect in Ni-Mn-Ga alloys offers potential for advanced actuator and sensor technologies.